Motif-based communities in complex networks

Arenas, Àlex; Fernández, Alberto; Fortunato, Santo; Gómez, Sergio

doi:10.1088/1751-8113/41/22/224001

Cited by 95 publications

(64 citation statements)

References 17 publications

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“…Another generalization of modularity was recently suggested by Arenas et al (Arenas et al, 2008a). They remarked that the fundamental unit to define modularity is the edge, but that high edge densities inside clusters usually imply the existence of long-range topological correlations between vertices, which are revealed by the presence of motifs (Milo et al, 2002), i.e.…”

Section: B Modifications Of Modularitymentioning

confidence: 99%

Community detection in graphs

2010

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The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.

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Section: B Modifications Of Modularitymentioning

confidence: 99%

Community detection in graphs

2010

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“…There has been some work in motif-based communities in complex networks [1] which like our work extends traditional notion of modularity introduced by Girvan and Newman [10]. The underlying motivation for motif-based community detection is that "the high density of edges within a community determines correlations between nodes going beyond nearest-neighbours," which is also our motivation for applying the influence-based modularity metric to community detection.…”

Section: Related Researchmentioning

confidence: 90%

“…Therefore, it does not take into account the characteristics of node types, that is 'who' are the nodes that a node is connected to and how influential these nodes are. In their paper on motif-based community detection, Arenas et al [1] state that the extended quality functions for-motif based modularity also obey the principle of the resolution limit. But this limit is now motif-dependent and then several resolution of substructures can be achieved by changing the motif.…”

Section: Related Researchmentioning

confidence: 99%

Community Detection Using a Measure of Global Influence

Ghosh¹,

Lerman²

2010

Advances in Social Network Mining and Analysis

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Abstract. The growing popularity of online social networks gave researchers access to large amount of network data and renewed interest in methods for automatic community detection. Existing algorithms, including the popular modularity-optimization methods, look for regions of the network that are better connected internally, e.g., have higher than expected number of edges within them. We believe, however, that edges do not give the true measure of network connectivity. Instead, we argue that influence, which we define as the number of paths, of any length, that exist between two nodes, gives a better measure of network connectivity. We use the influence metric to partition a network into groups or communities by looking for regions of the network where nodes have more influence over each other than over nodes outside the community. We evaluate our approach on several networks and show that it often outperforms the edge-based modularity algorithm.

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“…To answer to those questions, Newman and Girvan [46] introduce a measure of quality of a particular partition which they called modularity. The modularity is based on assortative mixing measure proposed by Newman [44]. The modularity is defined in the following way: Given a particular division of a network into k communities, let e denote a kxk matrix whose element is the fraction of all edges in the network which connects vertices in community i to those in community j.…”

Section: Quality Functionsmentioning

confidence: 99%