Abstract
Identifying cohesive subgraphs within networks is a fundamental problem in graph theory, relevant to various domains. The traditional clique problem, which finds fully connected subgraphs, often faces limitations due to its strict connectivity requirements. This paper introduces a novel degree-based relaxation model called Flexi-clique, where the degree constraint is adjusted sub-linearly based on the subgraph size. We establish that the maximum Flexi-clique problem is NP-hard and propose an efficient and effective peeling algorithm to address it. Our extensive experimental evaluation of real-world datasets demonstrates the effectiveness and efficiency of our approach in discovering large, cohesive subgraphs in networks.
| Original language | English |
|---|---|
| Title of host publication | CIKM 2024 - Proceedings of the 33rd ACM International Conference on Information and Knowledge Management |
| Publisher | Association for Computing Machinery |
| Pages | 3832-3836 |
| Number of pages | 5 |
| ISBN (Electronic) | 9798400704369 |
| DOIs | |
| Publication status | Published - 2024 Oct 21 |
| Event | 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024 - Boise, United States Duration: 2024 Oct 21 → 2024 Oct 25 |
Publication series
| Name | International Conference on Information and Knowledge Management, Proceedings |
|---|---|
| ISSN (Print) | 2155-0751 |
Conference
| Conference | 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024 |
|---|---|
| Country/Territory | United States |
| City | Boise |
| Period | 24/10/21 → 24/10/25 |
Bibliographical note
Publisher Copyright:© 2024 ACM.
Keywords
- cohesive subgraph discovery
- graph mining
ASJC Scopus subject areas
- General Business,Management and Accounting
- General Decision Sciences
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