Query-Centered Temporal Community Search via Time-Constrained Personalized PageRank

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 $q$ and other vertices but simply require the result to include $q$. Thus, they find many temporal irrelevant vertices (these vertices are called \emph{query-drifted vertices}) to $q$ for satisfying their cohesiveness, resulting in $q$ 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 $q$ and other vertices. Then, we introduce a model called $\beta$-temporal proximity core, which can combine temporal proximity and structural cohesiveness. Subsequently, our problem is formulated as an optimization task that finds a $\beta$-temporal proximity core with the largest $\beta$. To solve our problem, we first devise an exact and near-linear time greedy removing algorithm that iteratively removes unpromising vertices. To improve efficiency, we then design an approximate two-stage local search algorithm with bound-based pruning techniques. Finally, extensive experiments on eight real-life datasets and nine competitors show the superiority of the proposed solutions.