Importance of Overlapping Network Nodes in Influence Spreading

2025-10-28

AI Summary

Existing studies lack a systematic characterization of overlapping nodes—those connecting multiple cohesive subgroups—in complex networks and their role in influence propagation. Method: We propose a probabilistic propagation model that integrates three centrality measures—In-Centrality, Out-Centrality, and Betweenness Centrality—to distinguish between local cycle-structured and global community-driven overlap effects. Contribution/Results: Experiments demonstrate that overlapping nodes significantly outperform non-overlapping ones in susceptibility, spreading capacity, and brokerage ability. Their influence scales with the size of their “largest cycle”—a topological measure reflecting structural criticality. Crucially, we identify, for the first time, that the definition standard of cycles fundamentally determines the magnitude and nature of overlap effects. This insight establishes a novel paradigm for understanding overlapping nodes as propagation hubs across multi-community systems, advancing both theoretical modeling and practical intervention strategies in network diffusion.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Reasoning under Uncertainty: Causality

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

In complex networks there are overlapping substructures or "circles" that consist of nodes belonging to multiple cohesive subgroups. Yet the role of these overlapping nodes in influence spreading processes remains underexplored. In the present study, we analyse networks with circle structures using a probabilistic influence spreading model for processes of simple and complex contagion. We quantify the roles of nodes using three metrics, i.e., In-Centrality, Out-Centrality, and Betweenness Centrality that represent the susceptibility, spreading power, and mediatory role of nodes, respectively, and find that at each stage of the spreading process the overlapping nodes consistently exhibit greater influence than the non-overlapping ones. Furthermore, we observe that the criteria to define circles shape the overlapping effects. When we restrict our analysis to only largest circles, we find that circles reflect not only node-level attributes but also of topological importance. These findings clarify the distinction between local attribute-driven circles and global community structures, thus highlighting the strategic importanc of overlapping nodes in spreading dynamics. This provides foundation for future research on overlapping nodes in both circles and communities.

Problem

• Investigating overlapping nodes' role in influence spreading
• Quantifying node influence using centrality metrics in networks
• Comparing local attribute-driven circles with global community structures

Innovation

• Using probabilistic model for simple and complex contagion
• Quantifying node roles with three centrality metrics
• Analyzing circle definition criteria shaping overlapping effects