Towards Truss-Based Temporal Community Search
Huihui Yang, Chunxue Zhu, Longlong Lin, Pingpeng Yuan

TL;DR
This paper introduces a maximal-truss model for temporal community search that considers higher-order temporal connectivity, improving accuracy and efficiency in identifying dynamic communities in large networks.
Contribution
It proposes a novel maximal-truss model and a local search framework with pruning strategies, along with a temporal trussness index for scalable community detection.
Findings
Outperforms seven competitors in efficiency and effectiveness.
Demonstrates scalability on large real-world networks.
Provides a new approach considering higher-order temporal connectivity.
Abstract
Identifying communities from temporal networks facilitates the understanding of potential dynamic relationships among entities, which has already received extensive applications. However, existing methods primarily rely on lower-order connectivity (e.g., temporal edges) to capture the structural and temporal cohesiveness of the community, often neglecting higher-order temporal connectivity, which leads to sub-optimal results. To overcome this dilemma, we propose a novel temporal community model named maximal-truss (MDT). This model emphasizes maximal temporal support, ensuring all edges are connected by a sequence of triangles with elegant temporal properties. To search the MDT containing the user-initiated query node q (q-MDT), we first design a powerful local search framework with some effective pruning strategies. This approach aims to identify the solution from the small temporal…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsData Management and Algorithms · Peer-to-Peer Network Technologies · Web Data Mining and Analysis
