A Parallel Algorithm for Finding Robust Spanners in Large Social Networks

2025-08-02

AI Summary

In large-scale dynamic social networks, structural hole nodes exhibit insufficient robustness against node/edge failures and struggle to maintain inter-community connectivity. Method: This paper introduces “robust spanners”—hub nodes that preserve inter-community information dissemination under adversarial or stochastic node/edge failures. We propose a novel centrality metric integrating topological redundancy and cross-community bridging capacity, and develop an efficient CUDA-accelerated parallel detection algorithm. Results: Experiments on real-world social network datasets demonstrate that high-scoring nodes identified by our method simultaneously exhibit strong inter-community connectivity and high failure resilience. The GPU-accelerated implementation achieves an average 244× speedup over its CPU-based sequential counterpart, significantly enhancing scalability for identifying robust bridge nodes in dynamic network environments.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Natural Language Processing: Safety and Robustness
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Social Networks and Social Media: Social mining and social search on the Web

Abstract

Social networks, characterized by community structures, often rely on nodes called structural hole spanners to facilitate inter-community information dissemination. However, the dynamic nature of these networks, where spanner nodes may be removed, necessitates resilient methods to maintain inter-community communication. To this end, we introduce robust spanners (RS) as nodes uniquely equipped to bridge communities despite disruptions, such as node or edge removals. We propose a novel scoring technique to identify RS nodes and present a parallel algorithm with a CUDA implementation for efficient RS detection in large networks. Empirical analysis of real-world social networks reveals that high-scoring nodes exhibit a spanning capacity comparable to those identified by benchmark spanner detection algorithms while offering superior robustness. Our implementation on Nvidia GPUs achieves an average speedup of 244X over traditional spanner detection techniques, demonstrating its efficacy to identify RS in large social networks.

Problem

• Identify robust spanners in dynamic social networks
• Develop parallel algorithm for efficient RS detection
• Ensure inter-community communication despite node disruptions

Innovation

• Parallel algorithm for robust spanner detection
• CUDA implementation on Nvidia GPUs
• Novel scoring technique for RS nodes