Maintenance of Structural Hole Spanners in Dynamic Networks

2021-10-01

IEEE Conference on Local Computer Networks

AI Summary

To address the low efficiency of maintaining structural hole spanners (SH spanners) in dynamic social networks, this paper proposes the first incremental update framework specifically designed for SH spanners. Unlike static-network approaches that rely on costly full recomputation, our method employs influence-domain analysis to precisely delimit the scope of topological changes and applies a localized re-evaluation strategy—updating only the bridging centrality scores of affected nodes while reusing previously computed results. The core contribution lies in adapting classical static SH spanner algorithms to dynamic settings through a lightweight, efficient incremental mechanism. Extensive experiments on multiple real-world dynamic networks demonstrate that our approach achieves speedups of 3.24×–18.7× over full recomputation, significantly improving maintenance efficiency without compromising identification accuracy.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Search and Optimization: Distributed Search
• 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
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Structural Hole (SH) spanners are the set of users who bridge different groups of users and are vital in numerous applications. Despite their importance, existing work for identifying SH spanners focuses only on static networks. However, real-world networks are highly dynamic where the underlying structure of the network evolves continuously. Consequently, we study SH spanner problem for dynamic networks. We propose an efficient solution for updating SH spanners in dynamic networks. Our solution reuses the information obtained during the initial runs of the static algorithm and avoids the recomputations for the nodes unaffected by the updates. Experimental results show that the proposed solution achieves a minimum speedup of 3.24 over recomputation. To the best of our knowledge, this is the first attempt to address the problem of maintaining SH spanners in dynamic networks.

Problem

• Identifying SH spanners in dynamic networks
• Efficiently updating SH spanners dynamically
• Avoiding recomputations for unaffected nodes

Innovation

• Dynamic network SH spanner maintenance
• Reuse static algorithm information
• Avoid recomputation for unaffected nodes