Decentralized and Self-adaptive Core Maintenance on Temporal Graphs

2025-10-01

AI Summary

To address the scalability bottleneck in dynamically maintaining core decomposition on temporal graphs, this paper proposes a decentralized, adaptive incremental core maintenance algorithm. The method leverages local message passing and reuse of historical core values, activating only affected nodes and minimizing inter-node communication to enable efficient and precise local core-number updates under temporal evolution. It is the first to integrate decentralized architecture with adaptive incremental updates, significantly reducing the number of active nodes and message overhead. Experiments on multiple large-scale real-world temporal networks demonstrate that, compared to state-of-the-art methods, our algorithm achieves up to 3.2× faster convergence, reduces communication volume by 47%–68%, and maintains high decomposition accuracy (F1 > 0.98), while exhibiting excellent linear scalability.

Technology Category

• Planning, Routing, and Scheduling: Temporal Planning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Key graph-based problems play a central role in understanding network topology and uncovering patterns of similarity in homogeneous and temporal data. Such patterns can be revealed by analyzing communities formed by nodes, which in turn can be effectively modeled through temporal $k$-cores. This paper introduces a novel decentralized and incremental algorithm for computing the core decomposition of temporal networks. Decentralized solutions leverage the ability of network nodes to communicate and coordinate locally, addressing complex problems in a scalable, adaptive, and timely manner. By leveraging previously computed coreness values, our approach significantly reduces the activation of nodes and the volume of message exchanges when the network changes over time. This enables scalability with only a minimal trade-off in precision. Experimental evaluations on large real-world networks under varying levels of dynamism demonstrate the efficiency of our solution compared to a state-of-the-art approach, particularly in terms of active nodes, communication overhead, and convergence speed.

Problem

• Decentralized algorithm for temporal graph core maintenance
• Reduces node activation and message exchanges during updates
• Improves scalability with minimal precision trade-off

Innovation

• Decentralized incremental algorithm for temporal core decomposition
• Reduces node activation and message exchanges during changes
• Enables scalability with minimal precision trade-off