Accelerating K-Core Computation in Temporal Graphs

2025-08-19

AI Summary

For the problem of k-core enumeration within a given time window on temporal graphs, existing methods suffer from poor efficiency and limited scalability. This paper introduces, for the first time, the concept of *coreness count* and proposes an efficient algorithm based on precomputation of coreness counts and dynamic propagation. Leveraging the structural evolution of temporal graphs, the algorithm achieves theoretically optimal time complexity—its runtime depends solely on the output size. Precise pruning and incremental updates significantly reduce redundant computation. Extensive experiments on multiple real-world temporal graph datasets demonstrate that the proposed method outperforms the state-of-the-art by 1–2 orders of magnitude in runtime and reduces memory consumption by 40%–65%, achieving both high efficiency and strong scalability.

Technology Category

• Planning, Routing, and Scheduling: Temporal Planning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Content-based information diffusion

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.

Problem

• Efficiently enumerating temporal k-cores in graphs
• Improving scalability of temporal core computation
• Optimizing algorithm runtime for query time ranges

Innovation

• Uses core times for efficient computation
• Proves optimal bounded algorithmic running time
• Reduces cost by leveraging core times