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NetworkX is suitable for operation on large real-world graphs: e.g., graphs in excess of 10 million nodes and 100 million edges. [ clarification needed ] [ 19 ] Due to its dependence on a pure-Python "dictionary of dictionary" data structure, NetworkX is a reasonably efficient, very scalable , highly portable framework for network and social ...
The stochastic block model is a generative model for random graphs. This model tends to produce graphs containing communities, subsets of nodes characterized by being connected with one another with particular edge densities. For example, edges may be more common within communities than between communities.
The clique percolation method [1] is a popular approach for analyzing the overlapping community structure of networks.The term network community (also called a module, cluster or cohesive group) has no widely accepted unique definition and it is usually defined as a group of nodes that are more densely connected to each other than to other nodes in the network.
Networkx is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks; Graph-tool is a python module for efficient analysis of graphs. Its core data structures and algorithms are implemented in C++, with heavy use of Template metaprogramming, based on the Boost Graph Library.
A graph is called recurrent (or frequent) in G when its frequency F G (G′) is above a predefined threshold or cut-off value. We use terms pattern and frequent sub-graph in this review interchangeably. There is an ensemble Ω(G) of random graphs corresponding to the null-model associated to G.
There are a couple of software tools available that are able to compute clusterings in graphs with good modularity. Original implementation of the multi-level Louvain method. [14] The Leiden algorithm which additionally avoids unconnected communities. [15] The Vienna Graph Clustering (VieClus) algorithm, a parallel memetic algorithm. [16]
Simple weighting means that edges are weighted directly by the number of times the common association is repeated. (This is the method applied in the attached graph on the right.) This approach works fine for a wide range of settings such as molecular gastronomy or most social networks.
The Barabási–Albert (BA) model is an algorithm for generating random scale-free networks using a preferential attachment mechanism. Several natural and human-made systems, including the Internet, the World Wide Web, citation networks, and some social networks are thought to be approximately scale-free and certainly contain few nodes (called hubs) with unusually high degree as compared to ...