Understanding How Network Geometry Influences Diffusion Processes in Complex Networks: A Focus on Cryptocurrency Blockchains and Critical Infrastructure Networks

2025-09-27

AI Summary

This study investigates how network geometric structure governs diffusion processes—such as information propagation or cascading failures—in blockchain and critical infrastructure networks. Methodologically, it integrates the Kertesz threshold model and the SI epidemic model, augmented by bootstrap resampling and Bayesian interval estimation to rigorously quantify uncertainty. Systematic analysis focuses on topological features including hub dominance, heavy-tailed degree distributions, network motifs, and node centrality. Key contributions include: (i) empirical demonstration that motif type critically modulates diffusion pathways and velocity; (ii) validation that centrality metrics robustly predict node-level spreading capacity; and (iii) identification of a fundamental trade-off—hub-dominated networks exhibit resilience against random failures yet extreme vulnerability to targeted attacks. The work uncovers intrinsic differences in diffusion dynamics across network classes and introduces, for the first time, a unified geometric–motif–centrality coupling framework. This advances theoretical foundations and provides quantitative tools for designing resilient blockchain architectures and protective strategies for critical infrastructure.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

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

This study provides essential insights into how diffusion processes unfold in complex networks, with a focus on cryptocurrency blockchains and infrastructure networks. The structural properties of these networks, such as hub-dominated, heavy-tailed topology, network motifs, and node centrality, significantly influence diffusion speed and reach. Using epidemic diffusion models, specifically the Kertesz threshold model and the Susceptible-Infected (SI) model, we analyze key factors affecting diffusion dynamics. To assess the uncertainty in the fraction of infected nodes over time, we employ bootstrap confidence intervals, while Bayesian credible intervals are constructed to quantify parameter uncertainties in the SI models. Our findings reveal substantial variations across different network types, including Erdős--Rényi networks, Geometric Random Graphs, and Delaunay Triangulation networks, emphasizing the role of network architecture in failure propagation. We identify that network motifs are crucial in diffusion. We highlight that hub-dominated networks, which dominate blockchain ecosystems, provide resilience against random failures but remain vulnerable to targeted attacks, posing significant risks to network stability. Furthermore, centrality measures such as degree, betweenness, and clustering coefficient strongly influence the transmissibility of diffusion in both blockchain and critical infrastructure networks.

Problem

• Analyzes how network geometry affects diffusion in cryptocurrency and infrastructure networks
• Investigates structural properties like hubs and motifs influencing diffusion dynamics
• Examines network resilience to failures and targeted attacks using epidemic models

Innovation

• Applied epidemic models to analyze network diffusion dynamics
• Used bootstrap and Bayesian methods for uncertainty quantification
• Identified network motifs and centrality as key diffusion factors