Mining Diversified Top-r r Lasting Cohesive Subgraphs on Temporal Networks

Longlong Lin, Pingpeng Yuan*, Rong Hua Li, Hai Jin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

Temporal subgraph mining is recently ubiquitous. Identifying diversified and lasting ingredients is a fundamental problem in analyzing temporal networks. In this paper, we investigate the problem of finding diversified lasting cohesive subgraphs from temporal networks. Specifically, we first introduce a new model, called maximal lasting (κ,σ)-core, for characterizing lasting cohesive subgraphs on temporal networks so as to the nodes in the subgraph are connected densely and also the subgraph's structure remains unchanged for a period of time. To enhance the diversity of results, we then formulate a diversified lasting cohesive subgraphs problem, which finds R maximal lasting (κ,σ)-cores with maximum coverage regarding the number of vertices and timestamps. Unfortunately, we show that the optimization problem is NP-hard. To tackle this issue, we first devise a greedy algorithm named GreLC with (1-1/e) approximation ratio. However, GreLC has prohibitively high time and space complexity, resulting in poor scalability. Then, an improved DFS-based search algorithm called TopLC with 1/4 approximation ratio is proposed to lower the computational cost. Finally, empirical studies on six real-world temporal networks demonstrate that the proposed solutions perform efficiently and accurately, and our model is better than temporal cohesive subgraphs detected by existing approaches.

Original languageEnglish
Pages (from-to)1537-1549
Number of pages13
JournalIEEE Transactions on Big Data
Volume8
Issue number6
DOIs
Publication statusPublished - 1 Dec 2022

Keywords

  • Cohesive subgraphs
  • diversified top-r search
  • lasting pattern mining
  • temporal networks

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