A novel efficient method for tracking evolution of communities in dynamic networks

Abstract

Tracking community evolution can provide insights into significant changes in community interaction patterns, promote the understanding of structural changes, and predict the evolutionary behavior of networks. Therefore, it is a fundamental component of decision-making mechanisms in many fields such as marketing, public health, criminology, etc. However, in this problem domain, it is an open challenge to capture all possible events with high accuracy, memory efficiency, and reasonable execution times under a single solution. To address this gap, we propose a novel method for tracking the evolution of communities (TREC). TREC efficiently detects similar communities through a combination of Locality Sensitive Hashing and Minhashing. We provide experimental evidence on four benchmark datasets and real dynamic datasets such as AS, DBLP, Yelp, and Digg and compare them with the baseline work. The results show that TREC achieves an accuracy of about 98%, has a minimal space requirement, and is very close to the best performing work in terms of time complexity. Moreover, it can track all event types in a single solution.