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INSTALLING IGRAPH
Installing igraph from the Python Package Index¶. To ensure getting the latest binary release of igraph’s Python interface, it is recommended that you install it from the Python Package Index (PyPI), which has installers for Windows, Linux, and macOS. Currently there are binary packages for Python 2.7, and Python 3.5 through 3.8, but note that support for Python 2.7 will be discontinued IGRAPH REFERENCE MANUAL This function returns the modularity matrix defined as B_ij = A_ij - gamma * k_i * k_j / (2m) for undirected graphs, where A_ij is the adjacency matrix, gamma is the resolution parameter, k_i is the degree of vertex i, and m is the number of edges in the graph. When there are no edges, or the weights add up to zero, the result is undefined. IGRAPH R MANUAL PAGESSEE MORE ON IGRAPH.ORG IGRAPH R MANUAL PAGES For all_shortest_paths a list is returned, each list element contains a shortest path from from to a vertex in to.The shortest paths to the same vertex are collected into consecutive elements of the list. For mean_distance a single number is returned.. distance_table returns a named list with two entries: res is a numeric vector, the histogram of distances, unconnected is a numeric scalar, the IGRAPH R MANUAL PAGES The graph to convert. Gives how to create the adjacency matrix for undirected graphs. It is ignored for directed graphs. Possible values: upper: the upper right triangle of the matrix is used, lower: the lower left triangle of the matrix is used. both: the whole matrix is used, a symmetric matrix is returned. IGRAPH R MANUAL PAGES The closeness centrality of a vertex is defined by the inverse of the average length of the shortest paths to/from all the other vertices in the graph: 1/sum ( d (v,i), i != v) If there is no (directed) path between vertex \code {v} and \code {i} then the total number ofvertices is used in
IGRAPH R MANUAL PAGESSEE MORE ON IGRAPH.ORG IGRAPH R MANUAL PAGESSEE MORE ON IGRAPH.ORG WELCOME TO IGRAPH'S NEW HOME Release 0.9.0 of igraph’s C core has arrived, almost exactly one year after the latest minor release, 0.8.0. This release brings several new features and improvements, and also a complete switch from the old autotools-based build system to a new one based on CMake.This results in faster build times and the opportunity to use alternative build tools instead of make; in particular, igraph now IGRAPH R PACKAGEIGRAPH R MANUAL PAGESTKPLOTNETWORK ANALYSIS SOFTWAREIGRAPH C LIBRARYNEWSRGLPLOT The simplest way to install the igraph R package is typing install.packages ("igraph") in your R session. If you want to download the package manually, the following link leads you to the page of the latest release on CRAN where you can pick the appropriate source orINSTALLING IGRAPH
Installing igraph from the Python Package Index¶. To ensure getting the latest binary release of igraph’s Python interface, it is recommended that you install it from the Python Package Index (PyPI), which has installers for Windows, Linux, and macOS. Currently there are binary packages for Python 2.7, and Python 3.5 through 3.8, but note that support for Python 2.7 will be discontinued IGRAPH REFERENCE MANUAL This function returns the modularity matrix defined as B_ij = A_ij - gamma * k_i * k_j / (2m) for undirected graphs, where A_ij is the adjacency matrix, gamma is the resolution parameter, k_i is the degree of vertex i, and m is the number of edges in the graph. When there are no edges, or the weights add up to zero, the result is undefined. IGRAPH R MANUAL PAGESSEE MORE ON IGRAPH.ORG IGRAPH R MANUAL PAGES For all_shortest_paths a list is returned, each list element contains a shortest path from from to a vertex in to.The shortest paths to the same vertex are collected into consecutive elements of the list. For mean_distance a single number is returned.. distance_table returns a named list with two entries: res is a numeric vector, the histogram of distances, unconnected is a numeric scalar, the IGRAPH R MANUAL PAGES The graph to convert. Gives how to create the adjacency matrix for undirected graphs. It is ignored for directed graphs. Possible values: upper: the upper right triangle of the matrix is used, lower: the lower left triangle of the matrix is used. both: the whole matrix is used, a symmetric matrix is returned. IGRAPH R MANUAL PAGES The closeness centrality of a vertex is defined by the inverse of the average length of the shortest paths to/from all the other vertices in the graph: 1/sum ( d (v,i), i != v) If there is no (directed) path between vertex \code {v} and \code {i} then the total number ofvertices is used in
IGRAPH R MANUAL PAGESSEE MORE ON IGRAPH.ORG IGRAPH R MANUAL PAGESSEE MORE ON IGRAPH.ORGIGRAPH LIBRARY
igraph uses a CMake-based build system from version 0.9.0. It can be compiled and installed with the following list of commands: mkdir build && cd build cmake .. cmake --build . cmake --install . igraph is also available in several package managers: vcpkg: vcpkg install igraph. MacPorts (macOS): sudo port install igraph. IGRAPH R MANUAL PAGES Find the edge ids based on the incident vertices of the edges. Generate random graphs according to the G (n,m) Erdos-Renyi model. Generate random graphs according to the G (n,p) Erdos-Renyi model. Create an igraph graph from a list of edges, or a notable graph. Edgeconnectivity.
IGRAPH R MANUAL PAGES A vertex sequence is most often created by the V () function. The result of this includes all vertices in increasing vertex id order. A vertex sequence can be indexed by a numeric vector, just like a regular R vector. See [.igraph.vs and additional links to other vertex sequence operations below. IGRAPH R MANUAL PAGES Details. There are currently three different functions in the igraph package which can draw graph in various ways: plot.igraph does simple non-interactive 2D plotting to R devices. Actually it is an implementation of the plot generic function, so you can write plot (graph) instead of plot.igraph (graph). As it used the standard Rdevices it
IGRAPH R MANUAL PAGES Details. graph_from_data_frame creates igraph graphs from one or two data frames. It has two modes of operatation, depending whether the vertices argument is NULL or not.. If vertices is NULL, then the first two columns of d are used as a symbolic edge list and additional columns as edge attributes. The names of the attributes are taken from the names of the columns. IGRAPH R MANUAL PAGES From igraph version 0.5.1 this can be a sparse matrix created with the Matrix package. Character scalar, specifies how igraph should interpret the supplied matrix. See also the weighted argument, the interpretation depends on that too. Possible values are: directed , undirected, upper, lower, max, min , plus. See details below. IGRAPH R MANUAL PAGES For all_shortest_paths a list is returned, each list element contains a shortest path from from to a vertex in to.The shortest paths to the same vertex are collected into consecutive elements of the list. For mean_distance a single number is returned.. distance_table returns a named list with two entries: res is a numeric vector, the histogram of distances, unconnected is a numeric scalar, the IGRAPH REFERENCE MANUAL 2.1. igraph_shortest_paths — The length of the shortest paths between vertices. 2.2. igraph_shortest_paths_dijkstra — Weighted shortest path lengths between vertices. 2.3. igraph_shortest_paths_bellman_ford — Weighted shortest path lengths between vertices, allowing negative weights. 2.4. igraph_shortest_paths_johnson — Weighted shortest path lengthsbetween
IGRAPH R MANUAL PAGES The graph to convert. Gives how to create the adjacency matrix for undirected graphs. It is ignored for directed graphs. Possible values: upper: the upper right triangle of the matrix is used, lower: the lower left triangle of the matrix is used. both: the whole matrix is used, a symmetric matrix is returned. IGRAPH R MANUAL PAGES Centralization is a general method for calculating a graph-level centrality score based on node-level centrality measure. The formula for this is. C (G)=sum ( max (c (w), w) - c (v),v), where c (v) is the centrality of vertex v . The graph-level centrality score can be normalized by dividing by the maximum theoretical score for a graphwith the
WELCOME TO IGRAPH'S NEW HOME Release 0.9.0 of igraph’s C core has arrived, almost exactly one year after the latest minor release, 0.8.0. This release brings several new features and improvements, and also a complete switch from the old autotools-based build system to a new one based on CMake.This results in faster build times and the opportunity to use alternative build tools instead of make; in particular, igraph now IGRAPH R PACKAGEIGRAPH R MANUAL PAGESTKPLOTNETWORK ANALYSIS SOFTWAREIGRAPH C LIBRARYNEWSRGLPLOT Latest stable version – 1.2.6 The simplest way to install the igraph R package is typing install.packages("igraph") in your R session. If you want to download the package manually, the following link leads you to the page of the latest release on CRAN where you can pick the appropriate source or binary distribution yourself.IGRAPH LIBRARY
igraph uses a CMake-based build system from version 0.9.0. It can be compiled and installed with the following list of commands: mkdir build && cd build cmake .. cmake --build. cmake --install.. igraph is also available in several package managers: PYTHON-IGRAPH MANUAL Installation. igraph is on the Python Package Index with pre-compiled wheels for most Python distributions and platforms, so in most cases it can simply be installed using pip: $ pip install python-igraph The command above should attempt to download a pre-compiled wheel if your platform and Python version are among the supported ones. IGRAPH R MANUAL PAGES graph: The graph to convert. type: Gives how to create the adjacency matrix for undirected graphs. It is ignored for directed graphs. Possible values: upper: the upper right triangle of the matrix is used, lower: the lower left triangle of the matrix is used.both: thewhole matrix is
IGRAPH R MANUAL PAGES graph: The input graph. mode: This is the same as the mode argument of degree.. loops: Logical scalar, whether to consider loops edges when calculating the degree. normalized: Logical scalar. IGRAPH R MANUAL PAGES graph: The graph to analyze. vids: The vertices for which closeness will be calculated. mode: Character string, defined the types of the paths used for measuring the distance in directed graphs. “in” measures the paths to a vertex, “out” measures paths from a vertex, all uses undirected paths. This argument is ignored forundirected graphs.
PYTHON-IGRAPH API REFERENCE Converts a planar layout to a radial one. This method applies only to 2D layouts. The X coordinate of the layout is transformed to an angle, with min(x) corresponding to the parameter called min_angle and max(y) corresponding to max_angle.Angles are given in degrees, zero degree corresponds to the direction pointing upwards. IGRAPH R MANUAL PAGESSEE MORE ON IGRAPH.ORG IGRAPH R MANUAL PAGES Kleinberg's hub centrality scores. Description. The hub scores of the vertices are defined as the principal eigenvector of A*t(A), where A is the adjacency matrix of the graph.. Usage hub_score(graph, scale = TRUE, weights = NULL, options = arpack_defaults) WELCOME TO IGRAPH'S NEW HOME Release 0.9.0 of igraph’s C core has arrived, almost exactly one year after the latest minor release, 0.8.0. This release brings several new features and improvements, and also a complete switch from the old autotools-based build system to a new one based on CMake.This results in faster build times and the opportunity to use alternative build tools instead of make; in particular, igraph now IGRAPH R PACKAGEIGRAPH R MANUAL PAGESTKPLOTNETWORK ANALYSIS SOFTWAREIGRAPH C LIBRARYNEWSRGLPLOT Latest stable version – 1.2.6 The simplest way to install the igraph R package is typing install.packages("igraph") in your R session. If you want to download the package manually, the following link leads you to the page of the latest release on CRAN where you can pick the appropriate source or binary distribution yourself.IGRAPH LIBRARY
igraph uses a CMake-based build system from version 0.9.0. It can be compiled and installed with the following list of commands: mkdir build && cd build cmake .. cmake --build. cmake --install.. igraph is also available in several package managers: PYTHON-IGRAPH MANUAL Installation. igraph is on the Python Package Index with pre-compiled wheels for most Python distributions and platforms, so in most cases it can simply be installed using pip: $ pip install python-igraph The command above should attempt to download a pre-compiled wheel if your platform and Python version are among the supported ones. IGRAPH R MANUAL PAGES graph: The graph to convert. type: Gives how to create the adjacency matrix for undirected graphs. It is ignored for directed graphs. Possible values: upper: the upper right triangle of the matrix is used, lower: the lower left triangle of the matrix is used.both: thewhole matrix is
IGRAPH R MANUAL PAGES graph: The input graph. mode: This is the same as the mode argument of degree.. loops: Logical scalar, whether to consider loops edges when calculating the degree. normalized: Logical scalar. IGRAPH R MANUAL PAGES graph: The graph to analyze. vids: The vertices for which closeness will be calculated. mode: Character string, defined the types of the paths used for measuring the distance in directed graphs. “in” measures the paths to a vertex, “out” measures paths from a vertex, all uses undirected paths. This argument is ignored forundirected graphs.
PYTHON-IGRAPH API REFERENCE Converts a planar layout to a radial one. This method applies only to 2D layouts. The X coordinate of the layout is transformed to an angle, with min(x) corresponding to the parameter called min_angle and max(y) corresponding to max_angle.Angles are given in degrees, zero degree corresponds to the direction pointing upwards. IGRAPH R MANUAL PAGESSEE MORE ON IGRAPH.ORG IGRAPH R MANUAL PAGES Kleinberg's hub centrality scores. Description. The hub scores of the vertices are defined as the principal eigenvector of A*t(A), where A is the adjacency matrix of the graph.. Usage hub_score(graph, scale = TRUE, weights = NULL, options = arpack_defaults)INSTALLING IGRAPH
Installing igraph from the Python Package Index¶. To ensure getting the latest binary release of igraph’s Python interface, it is recommended that you install it from the Python Package Index (PyPI), which has installers for Windows, Linux, and macOS. Currently there are binary packages for Python 2.7, and Python 3.5 through 3.8, but note that support for Python 2.7 will be discontinued IGRAPH R MANUAL PAGES Details. is_connected decides whether the graph is weakly or strongly connected.. components finds the maximal (weakly or strongly) connected components of a graph.. count_components does almost the same as components but returns only the number of clusters found instead of returning the actual clusters.. component_distribution creates a histogram for the maximal connected component sizes. IGRAPH R MANUAL PAGES For all_shortest_paths a list is returned, each list element contains a shortest path from from to a vertex in to.The shortest paths to the same vertex are collected into consecutive elements of the list. For mean_distance a single number is returned.. distance_table returns a named list with two entries: res is a numeric vector, the histogram of distances, unconnected is a numeric scalar, the IGRAPH R MANUAL PAGES Details. modularity calculates the modularity of a graph with respect to the given membership vector.. The modularity of a graph with respect to some division (or vertex types) measures how good the division is, or how separated are the different vertex types from eachother.
IGRAPH R MANUAL PAGES graph: The input graph, should be directed. mode: Specifies how to use the direction of the edges. For “out”, the sorting order ensures that each node comes before all nodes to which it has edges, so nodes with no incoming edges go first.For “in”, it is quite the opposite: each node comes before all nodes from which it receives edges.Nodes with no outgoing edges go first. IGRAPH R MANUAL PAGES Centralization of a graph Description. Centralization is a method for creating a graph level centralization measure from the centrality scores of the vertices. IGRAPH R MANUAL PAGES Details. The Jaccard similarity coefficient of two vertices is the number of common neighbors divided by the number of vertices that are neighbors of at least one of the two vertices being considered. IGRAPH R MANUAL PAGES Kleinberg's hub centrality scores. Description. The hub scores of the vertices are defined as the principal eigenvector of A*t(A), where A is the adjacency matrix of the graph.. Usage hub_score(graph, scale = TRUE, weights = NULL, options = arpack_defaults) IGRAPH R MANUAL PAGES Find Bonacich alpha centrality scores of network positions Description. alpha_centrality calculates the alpha centrality of some (or all) vertices in a graph.. Usage alpha_centrality(graph, nodes = V(graph), alpha = 1, loops = FALSE, exo = 1, weights = NULL, tol = 1e-07, sparse = TRUE) IGRAPH R MANUAL PAGES Details. cliques find all complete subgraphs in the input graph, obeying the size limitations given in the min and max arguments.. largest_cliques finds all largest cliques in the input graph. A clique is largest if there is no other clique including more vertices. max_cliques finds all maximal cliques in the input graph. A clique in maximal if it cannot be extended to a larger clique. WELCOME TO IGRAPH'S NEW HOME Release 0.9.0 of igraph’s C core has arrived, almost exactly one year after the latest minor release, 0.8.0. This release brings several new features and improvements, and also a complete switch from the old autotools-based build system to a new one based on CMake.This results in faster build times and the opportunity to use alternative build tools instead of make; in particular, igraph now IGRAPH R PACKAGEIGRAPH R MANUAL PAGESTKPLOTNETWORK ANALYSIS SOFTWAREIGRAPH C LIBRARYNEWSRGLPLOT The simplest way to install the igraph R package is typing install.packages ("igraph") in your R session. If you want to download the package manually, the following link leads you to the page of the latest release on CRAN where you can pick the appropriate source orIGRAPH LIBRARY
igraph uses a CMake-based build system from version 0.9.0. It can be compiled and installed with the following list of commands: mkdir build && cd build cmake .. cmake --build . cmake --install . igraph is also available in several package managers: vcpkg: vcpkg PYTHON-IGRAPH MANUAL Installation. igraph is on the Python Package Index with pre-compiled wheels for most Python distributions and platforms, so in most cases it can simply be installed using pip: $ pip install python-igraph The command above should attempt to download a pre-compiled wheel if your platform and Python version are among the supported ones. IGRAPH R MANUAL PAGES The graph to convert. Gives how to create the adjacency matrix for undirected graphs. It is ignored for directed graphs. Possible values: upper: the upper right triangle of the matrix is used, lower: the lower left triangle of the matrix is used. both: the whole matrix is used, a symmetric matrix is returned. IGRAPH R MANUAL PAGES A named list with the following components: res. The node-level centrality scores. centralization. The graph level centrality index. theoretical_max. The maximum theoretical graph level centralization score for a graph with the given number of vertices, using the same parameters. If the normalized argument was TRUE, then the result wasdivided
IGRAPH REFERENCE MANUAL First, check the arguments of the functions and call IGRAPH_ERROR () if they are invalid. Second, call IGRAPH_FINALLY on each dynamically allocated object and call IGRAPH_FINALLY_CLEAN () with the proper argument before returning. Third, use IGRAPH_CHECK on IGRAPH R MANUAL PAGES The closeness centrality of a vertex is defined by the inverse of the average length of the shortest paths to/from all the other vertices in the graph: 1/sum ( d (v,i), i != v) If there is no (directed) path between vertex \code {v} and \code {i} then the total number ofvertices is used in
IGRAPH R MANUAL PAGESSEE MORE ON IGRAPH.ORG IGRAPH R MANUAL PAGES Optional positive weight vector for calculating weighted scores. If the graph has a weight edge attribute, then this is used by default. This function interprets edge weights as connection strengths. In the random surfer model, an edge with a larger weight is more likely to be selected by the surfer. A named list, to override some ARPACK options. WELCOME TO IGRAPH'S NEW HOME Release 0.9.0 of igraph’s C core has arrived, almost exactly one year after the latest minor release, 0.8.0. This release brings several new features and improvements, and also a complete switch from the old autotools-based build system to a new one based on CMake.This results in faster build times and the opportunity to use alternative build tools instead of make; in particular, igraph now IGRAPH R PACKAGEIGRAPH R MANUAL PAGESTKPLOTNETWORK ANALYSIS SOFTWAREIGRAPH C LIBRARYNEWSRGLPLOT The simplest way to install the igraph R package is typing install.packages ("igraph") in your R session. If you want to download the package manually, the following link leads you to the page of the latest release on CRAN where you can pick the appropriate source orIGRAPH LIBRARY
igraph uses a CMake-based build system from version 0.9.0. It can be compiled and installed with the following list of commands: mkdir build && cd build cmake .. cmake --build . cmake --install . igraph is also available in several package managers: vcpkg: vcpkg PYTHON-IGRAPH MANUAL Installation. igraph is on the Python Package Index with pre-compiled wheels for most Python distributions and platforms, so in most cases it can simply be installed using pip: $ pip install python-igraph The command above should attempt to download a pre-compiled wheel if your platform and Python version are among the supported ones. IGRAPH R MANUAL PAGES The graph to convert. Gives how to create the adjacency matrix for undirected graphs. It is ignored for directed graphs. Possible values: upper: the upper right triangle of the matrix is used, lower: the lower left triangle of the matrix is used. both: the whole matrix is used, a symmetric matrix is returned. IGRAPH R MANUAL PAGES A named list with the following components: res. The node-level centrality scores. centralization. The graph level centrality index. theoretical_max. The maximum theoretical graph level centralization score for a graph with the given number of vertices, using the same parameters. If the normalized argument was TRUE, then the result wasdivided
IGRAPH REFERENCE MANUAL First, check the arguments of the functions and call IGRAPH_ERROR () if they are invalid. Second, call IGRAPH_FINALLY on each dynamically allocated object and call IGRAPH_FINALLY_CLEAN () with the proper argument before returning. Third, use IGRAPH_CHECK on IGRAPH R MANUAL PAGES The closeness centrality of a vertex is defined by the inverse of the average length of the shortest paths to/from all the other vertices in the graph: 1/sum ( d (v,i), i != v) If there is no (directed) path between vertex \code {v} and \code {i} then the total number ofvertices is used in
IGRAPH R MANUAL PAGESSEE MORE ON IGRAPH.ORG IGRAPH R MANUAL PAGES Optional positive weight vector for calculating weighted scores. If the graph has a weight edge attribute, then this is used by default. This function interprets edge weights as connection strengths. In the random surfer model, an edge with a larger weight is more likely to be selected by the surfer. A named list, to override some ARPACK options. IGRAPH R MANUAL PAGES Details. There are currently three different functions in the igraph package which can draw graph in various ways: plot.igraph does simple non-interactive 2D plotting to R devices. Actually it is an implementation of the plot generic function, so you can write plot (graph) instead of plot.igraph (graph). As it used the standard Rdevices it
WELCOME TO PYTHON-IGRAPH’S DOCUMENTATION! Compiling python-igraph from source. Tutorial. Starting igraph. Creating a graph from scratch. Generating graphs. Setting and retrieving attributes. Structural properties of graphs. Querying vertices and edges based on attributes. Treating a graph as anadjacency matrix.
IGRAPH R MANUAL PAGES From igraph version 0.5.1 this can be a sparse matrix created with the Matrix package. Character scalar, specifies how igraph should interpret the supplied matrix. See also the weighted argument, the interpretation depends on that too. Possible values are: directed , undirected, upper, lower, max, min , plus. See details below. IGRAPH R MANUAL PAGES The modularity of a graph with respect to some division (or vertex types) measures how good the division is, or how separated are the different vertex types from each other. It defined as. Q=1/ (2m) * sum ( (Aij-ki*kj/ (2m) ) delta (ci,cj),i,j), here m is the number of edges, Aij is the element of the A adjacency matrix in row i andcolumn j
IGRAPH R MANUAL PAGES For all_shortest_paths a list is returned, each list element contains a shortest path from from to a vertex in to.The shortest paths to the same vertex are collected into consecutive elements of the list. For mean_distance a single number is returned.. distance_table returns a named list with two entries: res is a numeric vector, the histogram of distances, unconnected is a numeric scalar, the IGRAPH R MANUAL PAGES Details. is_connected decides whether the graph is weakly or strongly connected.. components finds the maximal (weakly or strongly) connected components of a graph.. count_components does almost the same as components but returns only the number of clusters found instead of returning the actual clusters.. component_distribution creates a histogram for the maximal connected component sizes. IGRAPH R MANUAL PAGES Arguments. The input graph, directed graphs will be considered as undirected ones, multiple edges and loops are ignored. Numeric constant, lower limit on the size of the cliques to find. NULL means no limit, ie. it is the same as 0. Numeric constant, upper limit on the size of the cliques IGRAPH R MANUAL PAGES The input graph, should be directed. mode. Specifies how to use the direction of the edges. For “ out ”, the sorting order ensures that each node comes before all nodes to which it has edges, so nodes with no incoming edges go first. For “ in ”, it is quite theopposite: each
IGRAPH R MANUAL PAGES The Dice similarity coefficient of two vertices is twice the number of common neighbors divided by the sum of the degrees of the vertices. Methof dice calculates the pairwise Dice similarities for some (or all) of the vertices. The inverse log-weighted similarity of two vertices is the number of their common neighbors, weighted by theinverse
IGRAPH R MANUAL PAGES Centralization is a general method for calculating a graph-level centrality score based on node-level centrality measure. The formula for this is. C (G)=sum ( max (c (w), w) - c (v),v), where c (v) is the centrality of vertex v . The graph-level centrality score can be normalized by dividing by the maximum theoretical score for a graphwith the
WELCOME TO IGRAPH'S NEW HOME Release 0.9.0 of igraph’s C core has arrived, almost exactly one year after the latest minor release, 0.8.0. This release brings several new features and improvements, and also a complete switch from the old autotools-based build system to a new one based on CMake.This results in faster build times and the opportunity to use alternative build tools instead of make; in particular, igraph now IGRAPH R PACKAGEIGRAPH R MANUAL PAGESTKPLOTNETWORK ANALYSIS SOFTWAREIGRAPH C LIBRARYNEWSRGLPLOT The simplest way to install the igraph R package is typing install.packages ("igraph") in your R session. If you want to download the package manually, the following link leads you to the page of the latest release on CRAN where you can pick the appropriate source orIGRAPH LIBRARY
igraph uses a CMake-based build system from version 0.9.0. It can be compiled and installed with the following list of commands: mkdir build && cd build cmake .. cmake --build . cmake --install . igraph is also available in several package managers: vcpkg: vcpkg PYTHON-IGRAPH MANUAL Installation. igraph is on the Python Package Index with pre-compiled wheels for most Python distributions and platforms, so in most cases it can simply be installed using pip: $ pip install python-igraph The command above should attempt to download a pre-compiled wheel if your platform and Python version are among the supported ones. IGRAPH R MANUAL PAGES The graph to convert. Gives how to create the adjacency matrix for undirected graphs. It is ignored for directed graphs. Possible values: upper: the upper right triangle of the matrix is used, lower: the lower left triangle of the matrix is used. both: the whole matrix is used, a symmetric matrix is returned. IGRAPH R MANUAL PAGES A named list with the following components: res. The node-level centrality scores. centralization. The graph level centrality index. theoretical_max. The maximum theoretical graph level centralization score for a graph with the given number of vertices, using the same parameters. If the normalized argument was TRUE, then the result wasdivided
IGRAPH REFERENCE MANUAL First, check the arguments of the functions and call IGRAPH_ERROR () if they are invalid. Second, call IGRAPH_FINALLY on each dynamically allocated object and call IGRAPH_FINALLY_CLEAN () with the proper argument before returning. Third, use IGRAPH_CHECK on IGRAPH R MANUAL PAGES The closeness centrality of a vertex is defined by the inverse of the average length of the shortest paths to/from all the other vertices in the graph: 1/sum ( d (v,i), i != v) If there is no (directed) path between vertex \code {v} and \code {i} then the total number ofvertices is used in
IGRAPH R MANUAL PAGESSEE MORE ON IGRAPH.ORG IGRAPH R MANUAL PAGES Optional positive weight vector for calculating weighted scores. If the graph has a weight edge attribute, then this is used by default. This function interprets edge weights as connection strengths. In the random surfer model, an edge with a larger weight is more likely to be selected by the surfer. A named list, to override some ARPACK options. WELCOME TO IGRAPH'S NEW HOME Release 0.9.0 of igraph’s C core has arrived, almost exactly one year after the latest minor release, 0.8.0. This release brings several new features and improvements, and also a complete switch from the old autotools-based build system to a new one based on CMake.This results in faster build times and the opportunity to use alternative build tools instead of make; in particular, igraph now IGRAPH R PACKAGEIGRAPH R MANUAL PAGESTKPLOTNETWORK ANALYSIS SOFTWAREIGRAPH C LIBRARYNEWSRGLPLOT The simplest way to install the igraph R package is typing install.packages ("igraph") in your R session. If you want to download the package manually, the following link leads you to the page of the latest release on CRAN where you can pick the appropriate source orIGRAPH LIBRARY
igraph uses a CMake-based build system from version 0.9.0. It can be compiled and installed with the following list of commands: mkdir build && cd build cmake .. cmake --build . cmake --install . igraph is also available in several package managers: vcpkg: vcpkg PYTHON-IGRAPH MANUAL Installation. igraph is on the Python Package Index with pre-compiled wheels for most Python distributions and platforms, so in most cases it can simply be installed using pip: $ pip install python-igraph The command above should attempt to download a pre-compiled wheel if your platform and Python version are among the supported ones. IGRAPH R MANUAL PAGES The graph to convert. Gives how to create the adjacency matrix for undirected graphs. It is ignored for directed graphs. Possible values: upper: the upper right triangle of the matrix is used, lower: the lower left triangle of the matrix is used. both: the whole matrix is used, a symmetric matrix is returned. IGRAPH R MANUAL PAGES A named list with the following components: res. The node-level centrality scores. centralization. The graph level centrality index. theoretical_max. The maximum theoretical graph level centralization score for a graph with the given number of vertices, using the same parameters. If the normalized argument was TRUE, then the result wasdivided
IGRAPH REFERENCE MANUAL First, check the arguments of the functions and call IGRAPH_ERROR () if they are invalid. Second, call IGRAPH_FINALLY on each dynamically allocated object and call IGRAPH_FINALLY_CLEAN () with the proper argument before returning. Third, use IGRAPH_CHECK on IGRAPH R MANUAL PAGES The closeness centrality of a vertex is defined by the inverse of the average length of the shortest paths to/from all the other vertices in the graph: 1/sum ( d (v,i), i != v) If there is no (directed) path between vertex \code {v} and \code {i} then the total number ofvertices is used in
IGRAPH R MANUAL PAGESSEE MORE ON IGRAPH.ORG IGRAPH R MANUAL PAGES Optional positive weight vector for calculating weighted scores. If the graph has a weight edge attribute, then this is used by default. This function interprets edge weights as connection strengths. In the random surfer model, an edge with a larger weight is more likely to be selected by the surfer. A named list, to override some ARPACK options. IGRAPH R MANUAL PAGES Details. There are currently three different functions in the igraph package which can draw graph in various ways: plot.igraph does simple non-interactive 2D plotting to R devices. Actually it is an implementation of the plot generic function, so you can write plot (graph) instead of plot.igraph (graph). As it used the standard Rdevices it
WELCOME TO PYTHON-IGRAPH’S DOCUMENTATION! Compiling python-igraph from source. Tutorial. Starting igraph. Creating a graph from scratch. Generating graphs. Setting and retrieving attributes. Structural properties of graphs. Querying vertices and edges based on attributes. Treating a graph as anadjacency matrix.
IGRAPH R MANUAL PAGES From igraph version 0.5.1 this can be a sparse matrix created with the Matrix package. Character scalar, specifies how igraph should interpret the supplied matrix. See also the weighted argument, the interpretation depends on that too. Possible values are: directed , undirected, upper, lower, max, min , plus. See details below. IGRAPH R MANUAL PAGES The modularity of a graph with respect to some division (or vertex types) measures how good the division is, or how separated are the different vertex types from each other. It defined as. Q=1/ (2m) * sum ( (Aij-ki*kj/ (2m) ) delta (ci,cj),i,j), here m is the number of edges, Aij is the element of the A adjacency matrix in row i andcolumn j
IGRAPH R MANUAL PAGES For all_shortest_paths a list is returned, each list element contains a shortest path from from to a vertex in to.The shortest paths to the same vertex are collected into consecutive elements of the list. For mean_distance a single number is returned.. distance_table returns a named list with two entries: res is a numeric vector, the histogram of distances, unconnected is a numeric scalar, the IGRAPH R MANUAL PAGES Details. is_connected decides whether the graph is weakly or strongly connected.. components finds the maximal (weakly or strongly) connected components of a graph.. count_components does almost the same as components but returns only the number of clusters found instead of returning the actual clusters.. component_distribution creates a histogram for the maximal connected component sizes. IGRAPH R MANUAL PAGES Arguments. The input graph, directed graphs will be considered as undirected ones, multiple edges and loops are ignored. Numeric constant, lower limit on the size of the cliques to find. NULL means no limit, ie. it is the same as 0. Numeric constant, upper limit on the size of the cliques IGRAPH R MANUAL PAGES The input graph, should be directed. mode. Specifies how to use the direction of the edges. For “ out ”, the sorting order ensures that each node comes before all nodes to which it has edges, so nodes with no incoming edges go first. For “ in ”, it is quite theopposite: each
IGRAPH R MANUAL PAGES The Dice similarity coefficient of two vertices is twice the number of common neighbors divided by the sum of the degrees of the vertices. Methof dice calculates the pairwise Dice similarities for some (or all) of the vertices. The inverse log-weighted similarity of two vertices is the number of their common neighbors, weighted by theinverse
IGRAPH R MANUAL PAGES Centralization is a general method for calculating a graph-level centrality score based on node-level centrality measure. The formula for this is. C (G)=sum ( max (c (w), w) - c (v),v), where c (v) is the centrality of vertex v . The graph-level centrality score can be normalized by dividing by the maximum theoretical score for a graphwith the
WELCOME TO IGRAPH'S NEW HOME Release 0.9.0 of igraph’s C core has arrived, almost exactly one year after the latest minor release, 0.8.0. This release brings several new features and improvements, and also a complete switch from the old autotools-based build system to a new one based on CMake.This results in faster build times and the opportunity to use alternative build tools instead of make; in particular, igraph now IGRAPH R PACKAGEIGRAPH R MANUAL PAGESTKPLOTNETWORK ANALYSIS SOFTWAREIGRAPH C LIBRARYNEWSRGLPLOT The simplest way to install the igraph R package is typing install.packages ("igraph") in your R session. If you want to download the package manually, the following link leads you to the page of the latest release on CRAN where you can pick the appropriate source orIGRAPH LIBRARY
igraph uses a CMake-based build system from version 0.9.0. It can be compiled and installed with the following list of commands: mkdir build && cd build cmake .. cmake --build . cmake --install . igraph is also available in several package managers: vcpkg: vcpkg IGRAPH R MANUAL PAGESSEE MORE ON IGRAPH.ORG IGRAPH R MANUAL PAGES For all_shortest_paths a list is returned, each list element contains a shortest path from from to a vertex in to.The shortest paths to the same vertex are collected into consecutive elements of the list. For mean_distance a single number is returned.. distance_table returns a named list with two entries: res is a numeric vector, the histogram of distances, unconnected is a numeric scalar, the IGRAPH R MANUAL PAGES The graph to convert. Gives how to create the adjacency matrix for undirected graphs. It is ignored for directed graphs. Possible values: upper: the upper right triangle of the matrix is used, lower: the lower left triangle of the matrix is used. both: the whole matrix is used, a symmetric matrix is returned. IGRAPH R MANUAL PAGES A named list with the following components: res. The node-level centrality scores. centralization. The graph level centrality index. theoretical_max. The maximum theoretical graph level centralization score for a graph with the given number of vertices, using the same parameters. If the normalized argument was TRUE, then the result wasdivided
IGRAPH REFERENCE MANUAL First, check the arguments of the functions and call IGRAPH_ERROR () if they are invalid. Second, call IGRAPH_FINALLY on each dynamically allocated object and call IGRAPH_FINALLY_CLEAN () with the proper argument before returning. Third, use IGRAPH_CHECK on IGRAPH R MANUAL PAGESSEE MORE ON IGRAPH.ORGR IGRAPH NETWORKR IGRAPH LAYOUTR IGRAPH PDFR IGRAPH PLOTIGRAPH NETWORK PLOTIGRAPH R PACKAGE IGRAPH R MANUAL PAGESSEE MORE ON IGRAPH.ORG WELCOME TO IGRAPH'S NEW HOME Release 0.9.0 of igraph’s C core has arrived, almost exactly one year after the latest minor release, 0.8.0. This release brings several new features and improvements, and also a complete switch from the old autotools-based build system to a new one based on CMake.This results in faster build times and the opportunity to use alternative build tools instead of make; in particular, igraph now IGRAPH R PACKAGEIGRAPH R MANUAL PAGESTKPLOTNETWORK ANALYSIS SOFTWAREIGRAPH C LIBRARYNEWSRGLPLOT The simplest way to install the igraph R package is typing install.packages ("igraph") in your R session. If you want to download the package manually, the following link leads you to the page of the latest release on CRAN where you can pick the appropriate source orIGRAPH LIBRARY
igraph uses a CMake-based build system from version 0.9.0. It can be compiled and installed with the following list of commands: mkdir build && cd build cmake .. cmake --build . cmake --install . igraph is also available in several package managers: vcpkg: vcpkg IGRAPH R MANUAL PAGESSEE MORE ON IGRAPH.ORG IGRAPH R MANUAL PAGES For all_shortest_paths a list is returned, each list element contains a shortest path from from to a vertex in to.The shortest paths to the same vertex are collected into consecutive elements of the list. For mean_distance a single number is returned.. distance_table returns a named list with two entries: res is a numeric vector, the histogram of distances, unconnected is a numeric scalar, the IGRAPH R MANUAL PAGES The graph to convert. Gives how to create the adjacency matrix for undirected graphs. It is ignored for directed graphs. Possible values: upper: the upper right triangle of the matrix is used, lower: the lower left triangle of the matrix is used. both: the whole matrix is used, a symmetric matrix is returned. IGRAPH R MANUAL PAGES A named list with the following components: res. The node-level centrality scores. centralization. The graph level centrality index. theoretical_max. The maximum theoretical graph level centralization score for a graph with the given number of vertices, using the same parameters. If the normalized argument was TRUE, then the result wasdivided
IGRAPH REFERENCE MANUAL First, check the arguments of the functions and call IGRAPH_ERROR () if they are invalid. Second, call IGRAPH_FINALLY on each dynamically allocated object and call IGRAPH_FINALLY_CLEAN () with the proper argument before returning. Third, use IGRAPH_CHECK on IGRAPH R MANUAL PAGESSEE MORE ON IGRAPH.ORGR IGRAPH NETWORKR IGRAPH LAYOUTR IGRAPH PDFR IGRAPH PLOTIGRAPH NETWORK PLOTIGRAPH R PACKAGE IGRAPH R MANUAL PAGESSEE MORE ON IGRAPH.ORG IGRAPH R MANUAL PAGES Details. There are currently three different functions in the igraph package which can draw graph in various ways: plot.igraph does simple non-interactive 2D plotting to R devices. Actually it is an implementation of the plot generic function, so you can write plot (graph) instead of plot.igraph (graph). As it used the standard Rdevices it
IGRAPH R MANUAL PAGES A vertex sequence is most often created by the V () function. The result of this includes all vertices in increasing vertex id order. A vertex sequence can be indexed by a numeric vector, just like a regular R vector. See [.igraph.vs and additional links to other vertex sequence operations below. IGRAPH R MANUAL PAGES From igraph version 0.5.1 this can be a sparse matrix created with the Matrix package. Character scalar, specifies how igraph should interpret the supplied matrix. See also the weighted argument, the interpretation depends on that too. Possible values are: directed , undirected, upper, lower, max, min , plus. See details below. IGRAPH R MANUAL PAGES For all_shortest_paths a list is returned, each list element contains a shortest path from from to a vertex in to.The shortest paths to the same vertex are collected into consecutive elements of the list. For mean_distance a single number is returned.. distance_table returns a named list with two entries: res is a numeric vector, the histogram of distances, unconnected is a numeric scalar, the IGRAPH R MANUAL PAGES The closeness centrality of a vertex is defined by the inverse of the average length of the shortest paths to/from all the other vertices in the graph: 1/sum ( d (v,i), i != v) If there is no (directed) path between vertex \code {v} and \code {i} then the total number ofvertices is used in
IGRAPH R MANUAL PAGES Details. is_connected decides whether the graph is weakly or strongly connected.. components finds the maximal (weakly or strongly) connected components of a graph.. count_components does almost the same as components but returns only the number of clusters found instead of returning the actual clusters.. component_distribution creates a histogram for the maximal connected component sizes. IGRAPH R MANUAL PAGES Centralization is a general method for calculating a graph-level centrality score based on node-level centrality measure. The formula for this is. C (G)=sum ( max (c (w), w) - c (v),v), where c (v) is the centrality of vertex v . The graph-level centrality score can be normalized by dividing by the maximum theoretical score for a graphwith the
IGRAPH R MANUAL PAGES The Dice similarity coefficient of two vertices is twice the number of common neighbors divided by the sum of the degrees of the vertices. Methof dice calculates the pairwise Dice similarities for some (or all) of the vertices. The inverse log-weighted similarity of two vertices is the number of their common neighbors, weighted by theinverse
IGRAPH R MANUAL PAGES There are several generalizations of transitivity to weighted graphs, here we use the definition by A. Barrat, this is a local vertex-level quantity, its formula is. weighted C_i = 1/s_i 1/ (k_i-1) sum ( (w_ij+w_ih)/2 a_ij a_ih a_jh, j, h) s_i is the strength of vertex i,see strength, a_ij
IGRAPH R MANUAL PAGES Optional positive weight vector for calculating weighted scores. If the graph has a weight edge attribute, then this is used by default. This function interprets edge weights as connection strengths. In the random surfer model, an edge with a larger weight is more likely to be selected by the surfer. A named list, to override some ARPACK options.igraph
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IGRAPH – THE NETWORK ANALYSIS PACKAGE igraph is a collection of network analysis tools with the emphasis on EFFICIENCY, PORTABILITY and ease of use. igraph is OPEN SOURCE and free. igraph can be programmed in R, PYTHON, MATHEMATICA and C/C++. ------------------------- igraph R package python-igraph IGraph/Migraph C library
* C/igraph 0.9.4
* C/igraph 0.9.3
* C/igraph 0.9.2
* python-igraph 0.9.1* C/igraph 0.9.1
* C/igraph 0.9.0
* C/igraph 0.8.5
* C/igraph 0.8.4
* python-igraph 0.8.3* C/igraph 0.8.3
* All news →
RECENT NEWS
C/IGRAPH 0.9.4
May 31st, 2021
C/igraph 0.9.4, the fourth bugfix release of the 0.9 series, has arrived. The source can be obtained from the GitHub releases page.
This release includes bug fixes only.CHANGED
* Unweighted transitivity (i.e. clustering coefficient) calculations now ignore multi-edges and edge directions instead of rejecting multigraphs and directed graphs. * igraph_transitivity_barrat() now returns an error code if the input graph has multiple edges (which is not handled correctly by the implementation yet).FIXED
* igraph_local_scan_k_ecount() now handles loops correctly. * igraph_transitivity_avglocal_undirected() is no longer slower than igraph_transitivity_local_undirected(). * Worked around an invalid warning issued by Clang 9.0 when compiling with OpenMP.OTHER
* Documentation improvements. -------------------------C/IGRAPH 0.9.3
May 5th, 2021
C/igraph 0.9.3, the third bugfix release of the 0.9 series, has arrived. The source can be obtained from the GitHub releases page.
This release includes bug fixes only.ADDED
* OpenMP is now enabled and used by certain functions (notably PageRank calculation) when the compiler supports it. Set IGRAPH_OPENMP_SUPPORT=OFF at configuration time to disable this.FIXED
* igraph_get_incidence() no longer reads and writes out of bounds when given a non-bipartite graph, but gives a warning and ignores edges within a part. * igraph_dyad_census() no longer reports an overflow on singleton graphs, and handles loops and multigraphs correctly. Undirected graphs are handled consistently and will no longer give a warning. * igraph_vector_lex_cmp() and igraph_vector_colex_cmp() dereferenced their arguments only once instead of twice, and therefore did not work with igraph_vector_ptr_sort(). * igraph_maximal_cliques_subset() and igraph_transitivity_barrat() corrupted the error handling stack (“finally stack”) under somecircumstances.
* CMake package files did not respect CMAKE_INSTALL_LIBDIR. This only affected Linux distributions which install into lib64 or other locations instead of lib. * The parser sources could not be generated when igraph was in a location that contained spaces in its path. * igraph no longer links to the math library (libm) when this is notnecessary.
* _CRT_SECURE_NO_WARNINGS is now defined during compilation to enable compatibility with UWP. * Fixed a compilation issue on MSYS / MinGW when link-time optimization was enabled and the MSYS Makefiles CMake generator was used. Some source files in igraph were renamed as a consequence, but these should not affect users of the library.DEPRECATED
* igraph_rng_min() is now deprecated; assume a constant zero as its return value if you used this function in your own code.OTHER
* Updated the vendored CXSparse library to version 3.2.0 -------------------------C/IGRAPH 0.9.2
Apr 14th, 2021
C/igraph 0.9.2, the second bugfix release of the 0.9 series, has arrived. The source can be obtained from the GitHub releases page.
This release includes bug fixes only. We have also added support for CMake package files, making it easier to use igraph in CMake-basedprojects.
ADDED
* CMake package files are now installed with igraph. This allows find_package(igraph) to find igraph and detect the appropriate compilation options for projects that link to it.FIXED
* igraph can now be used as a CMake subproject in other CMake-basedprojects.
* The documentaton can now be built from the release tarball. * Configuration will no longer fail when the release tarball is extracted into a subdirectory of an unrelated git repository. * The generated pkg-config file was incorrect when CMAKE_INSTALL_OTHER
* Improved installation instructions and tutorial. ------------------------- PYTHON-IGRAPH 0.9.1Mar 26th, 2021
python-igraph 0.9.1, the first bugfix release of the 0.9 series, has arrived. The preferred way of installing the Python interface is via pip; typing pip install python-igraph should install a pre-compiled Python wheel on most supported platforms (Windows, Linux and macOS). The pre-compiled wheels and the source code are also available from the Python Package Index page.
This release brings python-igraph up-to-date with igraph 0.9.1. See the changelogs on Github for relevant changes in 0.9.0 and 0.9.1:*
Changelog for 0.9.0
*
Changelog for 0.9.1
-------------------------C/IGRAPH 0.9.1
Mar 23rd, 2021
C/igraph 0.9.1, the first bugfix release of the 0.9 series, has arrived. The source can be obtained from the GitHub releases page.
This release includes bug fixes only, apart from a minor addition concerning lexicographic ordering of vectors, which was not substantial enough for a minor version bump. The changelog is below.ADDED
* igraph_vector_lex_cmp() and igrapg_vector_colex_cmp() for lexicographic and colexicographic comparison of vectors. These functions may also be used for sorting.CHANGED
* igraph_community_multilevel() is now randomized (thanks to DanielNoom ).
FIXED
* CMake settings that controlled the library installation directory name, such as CMAKE_INSTALL_LIBDIR, were not respected. * Under some conditions, the generated pkg-config file contained an incorrect include directory path. * The following functions were not exported from the shared library: igraph_subcomponent(), igraph_stack_ptr_free_all(), igraph_stack_ptr_destroy_all(), igraph_status_handler_stderr(), igraph_progress_handler_stderr(). * Built-in random number generators (igraph_rngtype_mt19937, igraph_rngtype_rand, igraph_rngtype_glibc2) were not exported from theshared library.
* igraph_layout_graphopt() no longer rounds the spring_length parameter to an integer. * igraph_get_all_shortest_paths_dijkstra() no longer modifies the res vector’s item destructor. * igraph_get_shortest_path_bellman_ford() did not work correctly when calculating paths to all vertices. * igraph_arpack_rnsolve() checks its parameters more carefully. * igraph_community_to_membership() does not crash anymore when csize is requested but membership is not. * igraph_citing_cited_type_game(): fixed memory leaks (thanks toDaniel Noom .
* igraph_transitivity_undirected(), igraph_transitivity_avglocal_undirected() and igraph_transitivity_barrat() no longer trigger an assertion failure when used with the null graph. * igraph_(personalized_)pagerank() would return incorrect results for weighted multigraphs with fewer than 128 vertices when using IGRAPH_PAGERANK_ALGO_PRPACK. * igraph_diversity() now checks its input more carefully, and throws an error when the input graph has multi-edges or is directed. * igraph_shortest_paths_johnson() would return incorrect results when the to argument differed from from (thanks to Daniel Noom). * igraph_is_graphical() would fail to set the result variable for certain special degree sequences in the undirected simple graph case. * Non-maximal clique finding functions would sometimes return incomplete results when finding more than 2147483647 (i.e. 2^31 - 1)cliques.
* GLPK internal errors no longer crash igraph. * Fixed some potential memory leaks that could happen on error conditions or when certain functions were interrupted. * When testing a DLL build on Windows, the PATH was sometimes not set correctly, causing the tests to fail. * When compiling from the git repository (as opposed to the release tarball), the build would fail with recent versions of bison and flex.OTHER
* Documentation improvements. * Much faster documentation builds. * Allow using a pre-generated arith.h header for f2c when cross-compiling; see the Installation section of the documentation. * The IGRAPH_ENABLE_LTO build option now supports the AUTO value, which uses LTO only if the compiler supports it. Warning: CMake may not always be able to detect that LTO is not fully supported. Therefore, the default setting is OFF. * The following functions are now interruptible: igraph_grg_game(), igraph_sbm_game(), igraph_barabasi_game(), igraph_barabasi_aging_game(). * Functions that use GLPK, such as igraph_feedback_arc_set() and igraph_community_optimal_modularity() are now interruptible. * Add support for older versions of Clang that do not recognize the-Wno-varargs flag.
ACKNOWLEDGMENTS
* Big thanks to Daniel Noom for continuing to expand the test suite and discovering and fixing several bugs in the process! -------------------------C/IGRAPH 0.9.0
Feb 16th, 2021
Release 0.9.0 of igraph’s C core has arrived, almost exactly one year after the latest minor release, 0.8.0. This release brings several new features and improvements, and also a complete switch from the old autotools-based build system to a new one based on CMake . This results in faster build times and the opportunity to use alternative build tools instead of make; in particular, igraph now support ninja and can also make use of a locally installed ccache compiler cache for even faster builds. We gratefully acknowledge support from CZI for the development of igraph. In addition, we would like to thank everyone who reported issues, contributed features or fixes, or edited the documentation. igraph is an open-source project run by volunteers. _As always, your contributions are very welcome!_ Most people use igraph through its high-level interfaces for R, Python or
Mathematica . A new release of the Python interface, based on 0.9.0, is expected to be released in a few days. The R and Mathematica interfaces already include some of the improvements, and will continue to integrate more. The sources and the changelog can be obtained from the GitHub releasespage .
-------------------------C/IGRAPH 0.8.5
Dec 7th, 2020
C/igraph 0.8.5, the fifth bugfix release of the 0.8 series, has arrived. The sources can be obtained from the GitHub releases page.
This release includes bug fixes only. The changelog is below.CHANGED
* igraph_write_graph_pajek(): the function now always uses the platform-native line endings (CRLF on Windows, LF on Unix and macOS). Earlier versions tried to enforce Windows line endings, but this was error-prone, and since all recent versions of Pajek support both line endings, enforcing Windows line endings is not necessary any more.FIXED
* Fixed several compilation issues with MINGW32/64, thanks to@jannick0
* igraph_layout_davidson_harel() was not interruptible; now it is. * Added a missing memory cleanup call in igraph_i_cattribute_combine_vertices(). * Fixed a few memory leaks in test cases. -------------------------C/IGRAPH 0.8.4
Nov 24th, 2020
C/igraph 0.8.4, the fourth bugfix release of the 0.8 series, has arrived. The sources can be obtained from the GitHub releases page.
This release includes bug fixes and documentation improvements. Thechangelog is below.
FIXED
* igraph_i_cattribute_combine_vertices(): fixed invalid cleanup code that eventually filled up the “finally” stack when combining vertices with attributes extensively. * igraph_hrg_sample(): fixed incorrect function prototype * igraph_is_posinf() and igraph_is_neginf(): fixed incorrect result on platforms where the sign of the result of isinf() is not indicative of the sign of the input. * Fixed building with vendored LAPACK and external BLAS * Fixed building with XCode 12.2 on macOSOTHER
* Documentation improvements * General code cleanup to reduce the number of compiler warnings ------------------------- PYTHON-IGRAPH 0.8.3Oct 8th, 2020
python-igraph 0.8.3, the third bugfix release of the 0.8 series, has arrived. The preferred way of installing the Python interface is via pip; typing pip install python-igraph should install a pre-compiled Python wheel on most supported platforms (Windows, Linux and macOS). The pre-compiled wheels and the source code are also available from the Python Package Index page.
This release brings python-igraph up-to-date with igraph 0.8.3, and it also contains bug fixes and performance improvements. This release will also be the last one whose version number is deliberately in sync with the version number of the C core. The changelog is below.ADDED
*
Graph.community_leiden() now supports a negative number of iterations; it will keep on iterating until the algorithm reaches a stablepartition.
*
Graph.Incidence() now supports weighted graphs, thanks to @PuneethaPai*
You can now create a Graph object from a pandas dataframe with Graph.DataFrame, thanks to @iosonofabio*
Added conversion to/from networkx and graph-tool with Graph.{to,from}_{networkx,graph_tool}, thanks to @iosonofabio*
Graph.add_edges() and Graph.add_vertices() now supports specifying attributes for the newly added vertices and edges, thanks to@iosonofabio
*
Added Graph.dfs() and Graph.dfsiter(), thanks to @iosonofabio*
Added Graph.bridges() to get the bridges of a graph.*
Added Graph.clear() to remove all edges, vertices and graph attributes in a single call, thanks to @iosonofabioCHANGED
*
Upgraded igraph C library to 0.8.3.*
Graph union and intersection now supports graphs with vertex names, thanks to @iosonofabio*
Graph.delete_vertices(None) and Graph.delete_edges(None) are now deprecated; use Graph.delete_vertices() and Graph.delete_edges() (without a positional argument) instead to delete all vertices or edges. The old syntax will be removed in 0.9.FIXED
*
setup.py now works on Windows with MinGW, thanks to @Parakoopa*
Graph.difference() now treats loop edges correctly*
summary() does not fail with non-string vertex names any more, thanksto @deeenes
*
The Reingold-Tilford layout should not produce edge crossings on treegraphs any more.
*
The postprocessing step of Graph.biconnected_components() is much faster now (#281); the earlier version was responsible for a significant speed difference between the C core and the Pythoninterface.
OTHER
*
Various documentation improvements, thanks to @chrisfalter, @iosonofabio
, @kmankinen
, @remysucre
, @szhorvat
*
Graph.complementer() now allows keyword arguments (it used to support positional arguments only).*
Dropped support for Python 3.5, added support for Python 3.9. -------------------------C/IGRAPH 0.8.3
Oct 2nd, 2020
C/igraph 0.8.3, the third bugfix release of the 0.8 series, has arrived. The sources can be obtained from the GitHub releases page.
This release includes bug fixes and performance improvements. Thechangelog is below.
ADDED
* igraph_vector_binsearch_slice() performs binary search on a sortedslice of a vector.
CHANGED
* igraph_eigenvector_centrality() assumes the adjacency matrix of undirected graphs to have twice the number of self-loops for each vertex on the diagonal. This makes the results consistent between an undirected graph and its directed equivalent when each edge is replaced by a mutual edge pair.FIXED
* igraph_isomorphic() now verifies that the input graphs have nomulti-edges.
* igraph_difference() was creating superfluous self loops. * igraph_count_multiple() was giving incorrect results for self-loops in directed graph. * igraph_betweenness_estimate(): fixed incorrect results with finitecutoff.
* igraph_eigen_matrix_symmetric(): fixed incorrect matrixmultiplication.
* Corrected several issues that could arise during an errorcondition.
* igraph_realize_degree_sequence() did not correctly detect some non-graphical inputs. * igraph_is_graphical_degree_sequence(): fixed incorrect results inundirected case.
* igraph_community_leiden(): fixed incorrect result when self-loopsare present.
* igraph_eigenvector_centrality(): fixed incorrect value for isolated vertices in weighted graphs. * igraph_eigenvector_centrality(): corrected the handling ofself-loops.
* igraph_layout_reingold_tilford(): fixed an issue where branches of the tree would sometimes overlap.OTHER
* igraph_degree_sequence_game(): improved performance with IGRAPH_DEGSEQ_SIMPLE_NO_MULTIPLE_UNIFORM method. * Improved the robustness of the test suite. * Documentation improvements. * Improved error and warning messages. * Improved compatibility with recent versions of Microsoft Visual C. -------------------------ALL NEWS →
2003 – 2020 The igraph core team. • Code licensed under GNU GPL 2 or later, documentationunder GNU FDL.
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