A Parallel Algorithm for Finding Robust Spanners in Large Social Networks

TL;DR

This paper introduces a parallel CUDA-based algorithm to efficiently identify robust spanners in large social networks, ensuring resilient inter-community communication despite network disruptions.

Contribution

It presents a novel scoring method and a GPU-accelerated algorithm for detecting resilient spanners, improving speed and robustness over existing methods.

Findings

High-scoring nodes have spanning capacity similar to benchmark algorithms.

GPU implementation achieves 244X speedup over traditional methods.

The approach enhances robustness in social network communication.

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.