Accelerating K-Core Computation in Temporal Graphs

TL;DR

This paper introduces a new algorithm for efficiently enumerating all temporal k-cores in graphs over a specified time range, significantly improving scalability and performance.

Contribution

The paper proposes a novel algorithm based on core times for faster temporal k-core enumeration, with proven optimal running time bounds.

Findings

Algorithm's running time is bounded by the size of resulting k-cores.

Computing core times is significantly less costly than enumeration.

The approach improves scalability in temporal graph analysis.

Abstract

We address the problem of enumerating all temporal k-cores given a query time range and a temporal graph, which suffers from poor efficiency and scalability in the state-of-the-art solution. Motivated by an existing concept called core times, we propose a novel algorithm to compute all temporal $k$-cores based on core times and prove that the algorithmic running time is bounded by the size of all resulting temporal k-cores, which is optimal in this scenario. Meanwhile, we show that the cost of computing core times is much lower, which demonstrates the close relevance between our overall running time and the result size.