Robustness and Influence Dynamics in Complex Networks: A Unified Framework for Structural Resilience and Diffusion Processes
Tuan Nguyen Minh
Journal of Networking Technology, Vol. 17, No. 2, 2026.
DOI: https://doi.org/10.6025/jnt/2026/17/2/47-62
Link: https://www.dline.info/jnt/fulltext/v17n2/jntv17n2_1.pdf
Abstract:
The rapid expansion of large-scale complex networks has intensified the need to understand how structural
resilience and functional diffusion processes interact under perturbations. This paper presents a unified
framework that integrates percolation-theoretic robustness metrics with canonical influence propagation
models, addressing a critical gap in network science where structural stability and information flow are
typically analyzed in isolation. We formalize network robustness through the persistence of the giant connected
component, global efficiency, and a scalar robustness index, evaluating degradation under both
random failures and targeted attacks. Analytical foundations, including the Molloy Reed criterion and kshell
decomposition, are coupled with simulation-based protocols to quantify critical percolation thresholds
and fragmentation dynamics. Concurrently, we model diffusion using the Independent Cascade and
Linear Threshold frameworks, revealing inherent trade-offs between structural resilience and propagation
efficiency. Hubs accelerate the spread of information but represent critical vulnerabilities, while high clustering
enhances local fault tolerance yet may impede global diffusion. Through empirical analysis of realworld
scientific collaboration networks, we demonstrate that optimal network design requires contextaware
balancing of redundancy, centrality distribution, and connectivity patterns. The study establishes
practical design principles for engineering adaptive architectures that withstand stochastic disruptions and
adversarial attacks while maintaining functional diffusivity. Ultimately, this framework provides actionable
insights for developing resilient infrastructure, robust social platforms, and efficient communication
systems, with proposed future extensions to temporal and multiplex network dynamics.