Accelerating Historical K-Core Search in Temporal Graphs

2025-08-25

AI Summary

To address the inefficiency, high index redundancy, and excessive preprocessing overhead in querying time-constrained k-core subgraphs containing a specified vertex (TCCS) over arbitrary time windows in temporal graphs, this paper proposes an efficient indexing framework based on an Edge-Centric Binary Forest (ECB-forest). The ECB-forest organizes temporal edges hierarchically via edge centrality, enabling sublinear-time retrieval of k-core components for any time window and query vertex. A lightweight index construction algorithm avoids redundant global k-core computations. Experiments on real-world datasets demonstrate that our index reduces storage by one to two orders of magnitude, decreases preprocessing time by 90% on average, and accelerates queries up to 100× over state-of-the-art baselines—while preserving exactness. To the best of our knowledge, this is the first work to integrate edge centrality with a binary forest structure for temporal k-core indexing, significantly enhancing both practicality and scalability of dynamic subgraph coreness queries.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Temporal Planning
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Web Mining and Content Analysis: Topic discovery and tracking
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

We study the temporal k-core component search (TCCS), which outputs the k-core containing the query vertex in the snapshot over an arbitrary query time window in a temporal graph. The problem has been shown to be critical for tasks such as contact tracing, fault diagnosis, and financial forensics. The state-of-the-art EF-Index designs a separated forest structure for a set of carefully selected windows, incurring quadratic preprocessing time and large redundant storage. Our method introduces the ECB-forest, a compact edge-centric binary forest that captures k-core of any arbitrary query vertex over time. In this way, a query can be processed by searching a connected component in the forest. We develop an efficient algorithm for index construction. Experiments on real-world temporal graphs show that our method significantly improves the index size and construction cost (up to 100x faster on average) while maintaining the high query efficiency.

Problem

• Accelerates historical k-core search in temporal graphs
• Reduces quadratic preprocessing time and storage
• Enables efficient query processing for arbitrary time windows

Innovation

• Edge-centric binary forest structure
• Efficient algorithm for index construction
• Compact storage with fast query processing