Detecting and Tracking Community Dynamics in Evolutionary Networks

Abstract

Community structure or clustering is ubiquitous in many evolutionary networks including social networks, biological networks and financial market networks. Detecting and tracking community deviations in evolutionary networks can uncover important and interesting behaviors that are latent if we ignore the dynamic information. In biological networks, for example, a small variation in a gene community may indicate an event, such as gene fusion, gene fission, or gene decay. In contrast to the previous work on detecting communities in static graphs or tracking conserved communities in time-varying graphs, this paper first introduces the concept of community dynamics, and then shows that the baseline approach by enumerating all communities in each graph and comparing all pairs of communities between consecutive graphs is infeasible and impractical. We propose an efficient method for detecting and tracking community dynamics in evolutionary networks by introducing graph representatives and community representatives to avoid generating redundant communities and limit the search space. We measure the performance of the representative-based algorithm by comparison to the baseline algorithm on synthetic networks, and our experiments show that our algorithm achieves a runtime speedup of 11-46. The method has also been applied to two real-world evolutionary networks including Food Web and Enron Email. Significant and informative community dynamics have been detected in both cases.

Encyclopedia of Social Network Analysis and Mining

“…Most authors use these events, or equivalent ones, sometimes under different names, e.g. Form for Birth, Dissolve/Vanish for Death, Join/Expansion for Growth, Leave/Shrinking for Contraction (Asur et al 2009;Bródka et al 2013;Chen et al 2010;Greene et al 2010). Note that the methods proposed by Palla et al based on these events were originally used on overlapping communities, but they also apply to disjoint ones.…”

“…The most straightforward use of these events is to count them and study their evolution in order to characterize the community structure, and therefore the network dynamics (Asur et al 2009;Bródka et al 2013;Chen et al 2010;Greene et al 2010). However, they also allow defining the notions of Community Age, i.e.…”

“…Articles mentioned in this entry include applications to real-world social networks Balasque 2012, 2013;Stattner and Collard 2012), social media (Dugué et al 2015;Lancichinetti, Kivela, et al 2010;Leskovec et al 2008;Orman et al 2015), the Internet (Guimera and Amaral 2005;Lancichinetti, Kivela, et al 2010), the Web (or parts of it) (Asur et al 2009;Kashtan and Alon 2005;Lancichinetti, Kivela, et al 2010;Leskovec et al 2008), biological networks (Chen et al 2010;Guimera and Amaral 2005;Lancichinetti, Kivela, et al 2010), communication networks (Bródka et al 2013;Chen et al 2010;Greene et al 2010;Lancichinetti, Kivela, et al 2010;Palla et al 2007), collaboration networks (Asur et al 2009;Guimera and Amaral 2005;Orman et al 2015;Palla et al 2007;Tumminello et al 2011), transportation networks (Guimera and Amaral 2005), sale co-occurrence networks (Stattner and Collard 2013), electronic circuits (Kashtan and Alon 2005).…”

Cut

“…Kumar et al [10] presented a model of network growth to capture singletons, isolated communities, and a giant component. Chen [4] proposed a method to detect community dynamics based on graph representatives and community representatives. Wang [17] presented a corebased algorithm of tracking community evolution by finding the most important nodes in a community to represent that community.…”

pbitMCE: A bit-based approach for maximal clique enumeration on multicore processors

“…It is used in many computational biology applications including 3D protein structure alignment, detection of proteinprotein interaction complex and motif finding [1], [2], [3]. It is used in social network analysis to detect communities [4], [5], [6].…”