Significant Engagement Community Search on Temporal Networks: Concepts and Algorithms

TL;DR

This paper introduces new algorithms for community search in temporal networks, focusing on identifying significant engagement communities that include a query vertex, with improved efficiency demonstrated on real-world data.

Contribution

The paper proposes novel algorithms, TDGP and BULS, for community search in temporal networks, addressing the gap in existing static network methods.

Findings

TDGP algorithm effectively identifies communities in temporal networks.

BULS and its variants improve search efficiency.

Proposed methods outperform baselines on real-world datasets.

Abstract

Community search, retrieving the cohesive subgraph which contains the query vertex, has been widely touched over the past decades. The existing studies on community search mainly focus on static networks. However, real-world networks usually are temporal networks where each edge is associated with timestamps. The previous methods do not work when handling temporal networks. We study the problem of identifying the significant engagement community to which the user-specified query belongs. Specifically, given an integer k and a query vertex u, then we search for the subgraph H which satisfies (i) u $\in$ H; (ii) the de-temporal graph of H is a connected k-core; (iii) In H that u has the maximum engagement level. To address our problem, we first develop a top-down greedy peeling algorithm named TDGP, which iteratively removes the vertices with the maximum temporal degree. To boost the efficiency, we then design a bottom-up local search algorithm named BULS and its enhanced versions BULS+ and BULS*. Lastly, we empirically show the superiority of our proposed solutions on six real-world temporal graphs.