Detecting and Tracking Community Dynamics in Evolutionary Networks

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.

I. Introduction

Networks of dynamic systems can be highly clustered, or form community structures [18]. A community, defined as a collection of individual objects that interact unusually frequently, is a very common substructure in many networks [7], including social networks, metabolic and protein interaction networks, financial market networks, and even climate networks. In social networks, a community is a real social grouping sharing the same interests or background [7]. In biological networks, a community might represent a set of proteins that perform a distinct function together. Communities in financial market networks might denote groups of investors that own the same stocks, and communities in climate networks might indicate regions with a similar climate.