Sketch-based community detection in evolving networks

TL;DR

This paper introduces a sketch-based method for detecting and analyzing community evolution in large, dynamic networks, efficiently identifying key events like growth, merging, and splitting.

Contribution

It presents a novel sketch-based algorithm for real-time community detection in evolving networks, capable of handling large and imbalanced clusters efficiently.

Findings

Efficient detection of community events in large networks.

Improved handling of small clusters through balanced sketch construction.

Enhanced performance demonstrated on a new stochastic block model benchmark.

Abstract

We consider an approach for community detection in time-varying networks. At its core, this approach maintains a small sketch graph to capture the essential community structure found in each snapshot of the full network. We demonstrate how the sketch can be used to explicitly identify six key community events which typically occur during network evolution: growth, shrinkage, merging, splitting, birth and death. Based on these detection techniques, we formulate a community detection algorithm which can process a network concurrently exhibiting all processes. One advantage afforded by the sketch-based algorithm is the efficient handling of large networks. Whereas detecting events in the full graph may be computationally expensive, the small size of the sketch allows changes to be quickly assessed. A second advantage occurs in networks containing clusters of disproportionate size. The sketch is constructed such that there is equal representation of each cluster, thus reducing the possibility that the small clusters are lost in the estimate. We present a new standardized benchmark based on the stochastic block model which models the addition and deletion of nodes, as well as the birth and death of communities. When coupled with existing benchmarks, this new benchmark provides a comprehensive suite of tests encompassing all six community events. We provide analysis and a set of numerical results demonstrating the advantages of our approach both in run time and in the handling of small clusters.