Community structure

In the study of complex networks, a network is said to have community structure if the nodes of the network can be easily grouped into (potentially overlapping) sets of nodes such that each set of nodes is densely connected internally. In the particular case of non-overlapping community finding, this implies that the network divides naturally into groups of nodes with dense connections internally and sparser connections between groups. A more general definition, suitable also for the case of overlapping community finding, is to aim to place pairs of nodes in the same community if, and only if, they are connected to each other.

Contents

What is community structure?

Network Community Structure.svg

In the study of networks, such as computer and information networks, social networks or biological networks, a number of different characteristics have been found to occur commonly, including the small-world property, heavy-tailed degree distributions, and clustering, among others. Another common characteristic is community structure [1] [2] [3] . In the context of networks, community structure refers to the occurrence of groups of nodes in a network that are more densely connected internally than with the rest of the network, as shown in the example image to the right. This inhomogeneity of connections suggests that the network has certain natural divisions within it. Note that communities are often defined in terms of the partition of the set of vertices, that is each node is put into one and only one community, just as in the figure. This is a useful simplification but may not be appropriate in many cases.

Not all networks need display community structure. Many model networks, for example, such as the random graph and the Barabási–Albert model, do not display community structure.

Why are we interested in it?

Community structures are quite common in real networks. Social networks often include community groups (the origin of the term, in fact) based on common location, interests, occupation, etc. Metabolic networks have communities based on functional groupings. Citation networks form communities by research topic.[1] Being able to identify these sub-structures within a network can provide insight into how network function and topology affect each other.

Algorithms for finding communities

Finding communities within an arbitrary network can be a difficult task. The number of communities, if any, within the network is typically unknown and the communities are often of unequal size and/or density. Despite these difficulties, however, several methods for community finding have been developed and employed with varying levels of success.[3]

Minimum-cut method

One of the oldest algorithms for dividing networks into parts is the minimum-cut method (and variants such as ratio cut and normalized cut). This method sees use, for example, in load balancing for parallel computing in order to minimize communication between processor nodes.

In the minimum-cut method, the network is divided into a predetermined number of parts, usually of approximately the same size, chosen such that the number of edges between groups is minimized. The method works well in many of the applications for which it was originally intended but is less than ideal for finding community structure in general networks since it will find communities regardless of whether they are implicit in the structure, and it will find only a fixed number of them. [4]

Hierarchical clustering

Another method for finding community structures in networks is hierarchical clustering. In this method one defines a similarity measure quantifying some (usually topological) type of similarity between node pairs. Commonly used measures include the cosine similarity, the Jaccard index, and the Hamming distance between rows of the adjacency matrix. Then one groups similar nodes into communities according to this measure. There are several common schemes for performing the grouping, the two simplest being single-linkage clustering, in which two groups are considered separate communities if and only if all pairs of nodes in different groups have similarity lower than a given threshold, and complete linkage clustering, in which all nodes within every group have similarity greater than threshold.

Girvan–Newman algorithm

Another commonly used algorithm for finding communities is the Girvan–Newman algorithm.[1] This algorithm identifies edges in a network that lie between communities and then removes them, leaving behind just the communities themselves. The identification is performed by employing the graph-theoretic measure betweenness, which assigns a number to each edge which is large if the edge lies "between" many pairs of nodes.

The Girvan–Newman algorithm returns results of reasonable quality and is popular because it has been implemented in a number of standard software packages. But it also runs slowly, taking time O(m2n) on a network of n vertices and m edges, making it impractical for networks of more than a few thousand nodes .[5]

Modularity maximization

One of the most widely used methods for community detection is modularity maximization.[5] Modularity is a benefit function that measures the quality of a particular division of a network into communities. The modularity maximization method detects communities by searching over possible divisions of a network for one or more that have particularly high modularity. Since exhaustive search over all possible divisions is usually intractable, practical algorithms are based on approximate optimization methods such as greedy algorithms, simulated annealing, or spectral optimization, with different approaches offering different balances between speed and accuracy. [6] The usefulness of modularity optimization is however questionable: on the one hand, it has been shown that modularity optimization often fails to detect clusters smaller than some scale, depending on the size of the network (resolution limit [7] ); on the other hand the landscape of modularity values is characterized by a huge degeneracy of partitions with high modularity, close to the absolute maximum, which may be very different from each other [8]

The Louvain method

The Louvain method[9] is a greedy optimisation method that has proved to provide excellent results for a wide range of applications and is now one of the most widely used method. The method is implemented in many network analysis softwares, including NetworkX and Gephi. The method consists of two phases. First, it looks for "small" communities by optimizing modularity in a local way. Second, it aggregates nodes of the same community and builds a new network whose nodes are the communities. These steps are repeated iteratively until a maximum of modularity is attained.

Clique based methods

Cliques are subgraphs in which every node is connected to every other node in the clique. As nodes can not be more tightly connected than this, it is not surprising that there are many approaches to community detection in networks based on the detection of cliques in a graph and the analysis of how these overlap. Note that as a node can be a member of more than one clique, a node can be a member of more than one community in these methods giving an overlapping community structure.

One approach is to find the maximal cliques, that is find the cliques which are not the subgraph of any other clique. The classic algorithm to find these is the Bron-Kerbosch algorithm. The overlap of these can be used to define communities in several ways. The simplest is to consider only maximal cliques bigger than a minimum size (number of nodes). The union of these cliques then defines a subgraph whose components (disconnected parts) then define communities.[10] Such approaches are often implemented in social network analysis software such as UCInet.

The alternative approach to is to use cliques of fixed size, k. The overlap of these can be used to define a type of k-regular hypergraph or a structure which is a generalisation of the line graph (the case when k=2) known as a Clique graph.[11] The clique graphs have vertices which represent the cliques in the original graph while the edges of the clique graph record the overlap of the clique in the original graph. Applying any of the previous community detection methods (which assign each node to a community) to the clique graph then assigns each clique to a community. This can then be used to determine community membership of nodes in the cliques. Again as a node may be in several cliques, it can be a member of several communities. For instance the clique percolation method[12] defines communities as percolation clusters of k-cliques. To do this it finds all k-cliques in a network, that is all the complete sub-graphs of k-nodes. It then defines two k-cliques to be adjacent if they share k − 1 nodes, that is this is used to define edges in a clique graph. A community is then defined to be the maximal union of k-cliques in which we can reach any k-clique from any other k-clique through series of k-clique adjacencies. That is communities are just the connected components in the clique graph. Since a node can belong to several different k-clique percolation clusters at the same time, the communities can overlap with each other.

Testing methods of finding communities algorithms

Most research on community detection has been concerned with the development of new methods and relatively little attention has been paid to the testing of those methods, i.e., to checking how good their performance is compared with other methods. To the extent that testing has taken place, a number of approaches have been employed. For instance, researchers have compared the modularity values of different algorithms applied to the same networks to see which algorithms find the community structures with highest modularity. Another approach involves applying algorithms to networks that have known community structure, to see whether that structure can be recovered. A number of real-world networks have become standard benchmarks for such problems, particularly the so-called "karate club" network, a social network drawn from a well-known study of university students in the 1970s, and the "college football" network, a network of American football games between US universities. Real-world networks have the advantage of presenting realistic challenges to algorithms, but the disadvantage that the nature and difficulty of those challenges cannot easily be controlled by the experimenter. An alternative approach is to use a computer-generated network that has community structure deliberately placed in it by the experimenter. A common example of this approach is the "four groups" test, in which a network is divided into four equally-sized groups (usually of 32 nodes each) and the probabilities of connection within and between groups varied to create more or less challenging structures for the detection algorithm. Such benchmark graphs are a special case of the planted l-partition model [13] of Condon and Karp, or more generally of "stochastic block models," a general class of random network models containing community structure. Recently, other more flexible benchmarks have been proposed that allow for varying group sizes and nontrivial degree distributions, such as the benchmark of Lancichinetti et al. ,[14] which is an extension of the four groups benchmark that includes heterogeneous distributions of node degree and community size, making it a more severe test of community detection methods.

See also

External links

References

  1. ^ a b c M. Girvan and M. E. J. Newman (2002). "Community structure in social and biological networks". Proc. Natl. Acad. Sci. USA 99 (12): 7821–7826. doi:10.1073/pnas.122653799. PMC 122977. PMID 12060727. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=122977. 
  2. ^ S. Fortunato (2010). "Community detection in graphs". Phys. Rep. 486 (3-5): 75–174. doi:10.1016/j.physrep.2009.11.002. 
  3. ^ a b M. A. Porter, J.-P. Onnela and P. J. Mucha (2009). "Communities in Networks". Not. Amer. Math. Soc. 56: 1082–1097, 1164–1166. http://www.ams.org/notices/200909/rtx090901082p.pdf. 
  4. ^ M. E. J. Newman (2004). "Detecting community structure in networks". Eur. Phys. J. B 38 (2): 321–330. doi:10.1140/epjb/e2004-00124-y. 
  5. ^ a b M. E. J. Newman (2004). "Fast algorithm for detecting community structure in networks". Phys. Rev. E 69 (6): 066133. doi:10.1103/PhysRevE.69.066133. 
  6. ^ L. Danon, J. Duch, A. Díaz-Guilera and A. Arenas (2005). "Comparing community structure identification". J. Stat. Mech. 2005 (09): P09008. doi:10.1088/1742-5468/2005/09/P09008. 
  7. ^ S. Fortunato and M. Barthelemy (2007). "Resolution limit in community detection". Proceedings of the National Academy of Science of the USA 104 (1): 36–41. doi:10.1073/pnas.0605965104. PMC 1765466. PMID 17190818. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1765466. 
  8. ^ B. H. Good, Y.-A. de Montjoye and A. Clauset (2010). "The performance of modularity maximization in practical contexts". Phys. Rev. E 81 (4): 046106. doi:10.1103/PhysRevE.81.046106. 
  9. ^ V.D. Blondel, J.-L. Guillaume, R. Lambiotte and E. Lefebvre (2008). "Fast unfolding of community hierarchies in large networks". J. Stat. Mech. 2008 (10): P10008. doi:10.1088/1742-5468/2008/10/P10008. 
  10. ^ M.G. Everett and S.P. Borgatti (1998). "Analyzing Clique Overlap Connections". Connections 21: 49. 
  11. ^ T.S. Evans (2010). "Clique Graphs and Overlapping Communities". J. Stat. Mech.: P12037. arXiv:arXiv:1009.0638. doi:10.1088/1742-5468/2010/12/P12037. 
  12. ^ G. Palla, I. Derényi, I. Farkas and T. Vicsek (2005). "Uncovering the overlapping community structure of complex networks in nature and society". Nature 435 (7043): 814–818. doi:10.1038/nature03607. PMID 15944704. 
  13. ^ Condon, A.; Karp, R. M. (2001). "Algorithms for graph partitioning on the planted partition model". Random Struct. Algor. 18 (2): 116–140. doi:10.1002/1098-2418(200103)18:2<116::AID-RSA1001>3.0.CO;2-2. 
  14. ^ A. Lancichinetti, S. Fortunato and F. Radicchi (2008). "Benchmark graphs for testing community detection algorithms". Phys. Rev. E 78 (4): 046110. doi:10.1103/PhysRevE.78.046110. 

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