Accelerating Historical K-Core Search in Temporal Graphs

TL;DR

This paper introduces the ECB-forest, a compact data structure that significantly accelerates the search for k-core components in temporal graphs, reducing index size and construction time while maintaining high query efficiency.

Contribution

The paper proposes the ECB-forest, a novel edge-centric binary forest that efficiently captures k-core information over time, outperforming previous methods in size and speed.

Findings

Up to 100x faster index construction

Significantly smaller index size

Maintains high query efficiency

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.