Query-Centered Temporal Community Search via Time-Constrained Personalized PageRank
Longlong Lin, Pingpeng Yuan, Rong-Hua Li, Chunxue Zhu, Hongchao Qin,, Hai Jin, and Tao Jia

TL;DR
This paper introduces a novel query-centered temporal community search method that leverages time-constrained Personalized PageRank and a $eta$-temporal proximity core to improve relevance and efficiency over existing approaches.
Contribution
The paper proposes a new query-centered approach using Time-Constrained Personalized PageRank and $eta$-temporal proximity core, addressing query drift and computational complexity issues.
Findings
The proposed algorithms outperform nine competitors in experiments.
The methods achieve near-linear time complexity.
The approach effectively reduces query drift in temporal community search.
Abstract
Existing temporal community search suffers from two defects: (i) they ignore the temporal proximity between the query vertex and other vertices but simply require the result to include . Thus, they find many temporal irrelevant vertices (these vertices are called \emph{query-drifted vertices}) to for satisfying their cohesiveness, resulting in being marginalized; (ii) their methods are NP-hard, incurring high costs for exact solutions or compromised qualities for approximate/heuristic algorithms. Inspired by these, we propose a novel problem named \emph{query-centered} temporal community search to circumvent \emph{query-drifted vertices}. Specifically, we first present a novel concept of Time-Constrained Personalized PageRank to characterize the temporal proximity between and other vertices. Then, we introduce a model called -temporal proximity core, which can…
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Taxonomy
TopicsCaching and Content Delivery · Advanced Image and Video Retrieval Techniques · Data Management and Algorithms
