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Igraph library is a small library designed to help you create and manipulate graphs.It includes implementations for classic graph theory problems like minimum spanning trees and network flow, and also implements algorithms for some recent network analysis methods, like community structure search. There is simply no other graph library out there which can handle graphs of the size the author is confronted with. Whenever possible, igraph tries to be also user friendly and portable.
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Installing igraph Download the igraph library for your operating system from INSTALLING the igraph library on OSX: The igraph library needs to link with the boost C++ library which we can do using the macports system. On Mac OSX, macports offers the “ports utility” to install libraries. So, we’ll install the igraph library by typing the following: open /usr/local/bin sudo ports install boost sudo ports install igraph Listing igraph packages in macports To search for igraph packages, type: port -v list igraph In the macports output you should see many packages, e.g.: # See the igraph package list in macports ~ $ port -v list igraph ==> igraph 0.6.5 released ( [➜ igraph git:(master) libs/graphite] $ This shows the igraph version is 0.6.5 Igraph Library Porting To port igraph to other operating systems, use the commands in the port.mk file. This sets the correct make options. ==>./configure ==> make ==> make install If igraph is successful, then some files like “igraph.h” should be in the igraph source directory. If you get an error “port: Unknown error -1”, then there is something else that is wrong. Check the port.mk file. Or, if this is a new port, then check with the port maintainer. h-file files The igraph library consists of several files. The two files which are important are – “igraph.h” – “igraph_static.h” In the igraph library repository, you will find versions of both these two files for all operating systems. The files contain most of the functions needed by igraph. The igraph.h file is where you find the definitions of all the function in igraph. The function names are given in the lines like: #define igraph_VESSEL_INVERSE_GRAPH_PARAM_SEARCH The igraph_static.h is where you find the declarations and the macro definitions (similar to C’s #defines). You can view the (incomplete) source code of the ig
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The igraph library is a small library designed to help you create and manipulate graphs.It includes implementations for classic graph theory problems like minimum spanning trees and network flow, and also implements algorithms for some recent network analysis methods, like community structure search. There is simply no other graph library out there which can handle graphs of the size the author is confronted with. Whenever possible, igraph tries to be also user friendly and portable. There is a graphical interface, and you can look at the source code. This is the igraph API. The term “function” is used to denote any method that returns a value or modifies the graph: igraph_vector_attr(v, names, attrs); igraph_vector_attr_i(v, names, attrs); igraph_vector_attr_s(v, names, attrs); igraph_vector_attr_i_i(v, names, attrs); igraph_vector_attr_s_s(v, names, attrs); igraph_vector_attr_s_s_s(v, names, attrs); igraph_vector_attr_s_i(v, names, attrs); igraph_vector_attr_s_i_i(v, names, attrs); igraph_vector_attr_s_s_s(v, names, attrs); igraph_vector_attr_s_i(v, names, attrs); igraph_vector_attr_s_i_i(v, names, attrs); igraph_vector_attr_s_s_s(v, names, attrs); igraph_vector_attr_s_i(v, names, attrs); igraph_vector_attr_s_i_i(v, names, attrs); igraph_vector_attr_s_s_s(v, names, attrs); igraph_vector_attr_s_i(v, names, attrs); igraph_vector_attr_s_i_i(v, names, attrs); igraph_vector_attr_s_s_s(v, names, attrs); igraph_vector_attr_s_i(v, names, attrs); igraph_vector_attr_s_i_i(v, names, attrs); igraph_vector_attr_s_ 91bb86ccfa
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It has a small memory footprint (as explained in the FAQ below), it is portable and platform independent. It has object orientated interface: it is easy to write algorithms and processes. It supports the following types of graphs: Edge oriented graphs: simple (undirected) graphs and multi-graphs. Adjacency lists of integer numbers: directed graphs. Adjacency matrices and adjacency lists of integer and other data types: arbitrary graphs. Adjacency lists of vertices and edges: directed graphs. Adjacency matrices: arbitrary graphs. Adjacency matrices and adjacency lists of integer numbers: arbitrary graphs. Adjacency lists of integer numbers only: arbitrary graphs (ignoring null edges and self-loops). Adjacency lists of integer numbers only: weighted graphs. Adjacency lists of floating point numbers: arbitrary graphs. Adjacency lists of rational numbers: arbitrary graphs. Adjacency matrices: weighted graphs. Adjacency matrix of elements of a generic container: arbitrary graphs. I have tested that igraph does not use more than 150K of RAM under Linux or Mac. Important things to know: Igraph is designed to be a self-contained library. It does not rely on any external libraries or packages. See FAQs at the bottom of the page for more info. The igraph library is not a Java or C++ API. It is designed to be used directly in your code. Graphs can be stored with several formats: edgelist, adj, mat: all of these correspond to a NetworkX graph. They are very easy to work with, and may also seem a bit faster if you have a lot of graphs with the same type of structure. graphviz: I recommend this format for visualizing graphs, especially with dot. Igraph comes with a bunch of small demos. You may also need some other files: igraph.h file (used internally, for portability) igraph.h file (used internally, for portability) igraph_gst.h file (used internally, for portability) graph.h file (a small graph library) igraph_all.h file (used internally, for portability) igraph_util.h file (used internally, for portability) graphviz/graph.h file (used
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This library combines the classic graph theory problems (Minimum Spanning Tree, Max-Flow, Shortest Path,…) with two algorithm to solve community detection and global similarity detection. Community Detection: The main idea is that you would like to know how to split your graph into meaningful communities. Community Detection algorithms are all about finding those groups of nodes which are dense inside the graph, but which have sparse connections to the other nodes. The output of this algorithm is just a list of subgraphs with a set of rules which would qualify a subgraph as belonging to the same community as the main graph. Global Similarity: The main idea of this library is to find groups of objects in a graph, which are closely related and which behave and look similar to each other. Global Similarity Search algorithms are all about searching for objects which are similar to each other and which happen to form dense groups in a graph. The output of this algorithm is just a list of clusters where the objects in every cluster are considered similar to each other. The libraries solves the problem both for undirected graphs and for directed graphs. Also, igraph can handle graphs of different types, as well as hypercubes, tree, clique, square,circle, and other graphs, using the client_metrics parameter. If the graph is not a graph but a vector of adjacency values, the client_metrics parameter will have no effect. Algorithms implemented Minimum Spanning Tree: Algorithm implemented using Prim’s algorithm and Kruskal’s algorithm. Maximum Flow: Algorithm implemented using Ford-Fulkerson algorithm and Shortest Path. Shortest Path: Algorithm implemented using Dijkstra’s algorithm. Communities and graph similarity: Communities are detected using Louvain algorithm and Very Fast algorithm. Graph similarity algorithms are implemented using Louvain algorithm. Client metrics: If the graph is a graph or a matrix of adjacency values, the client_metrics parameter will have no effect. Performance: O(n + m) is the worst case where n and m are the number of edges and nodes in the graph, respectively. References: https
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