Aug 2026· 2026 International Conference on Intelligent Multimedia, Networking, and Security (IMNS)· pp. 1-6· 0 citations· 19 references
Abstract
Identifying influential nodes in complex networks is a fundamental problem with applications in information diffusion, epidemic control, infrastructure robustness, and biological systems. Traditional approaches rely on structural centrality measures, such as degree, betweenness, closeness, and PageRank, which quantify node importance based on network connectivity. However, these measures do not explicitly account for diffusion dynamics and the structural impact of node removal, where both spreading capability and network resilience play a critical role. In this paper, we propose a unified framework that jointly captures diffusion-based influence and structural resilience. We first introduce an SIR-based centrality in which node influence is defined by its spreading capability, while resilience is quantified by measuring the change in total network diffusion after node removal. To address the computational cost of this formulation, we propose the Resilient-Influential Node (RIN) centrality, which efficiently approximates the unified objective by combining classical centrality measures with a Laplacian-based structural adjustment. Experimental results on multiple real-world networks, using SIR-based rankings as ground truth, show that the proposed RIN framework provides a consistent and principled characterization of influential and resilient nodes across diverse network structures.
This study introduces a new ranking framework that integrates a quasi-Laplacian structural measure with a gravity-inspired aggregation process and demonstrates that the proposed framework consistently outperforms existing techniques in terms of accuracy, resolution, and computational simplicity.
Experimental results in seven complex networks of the real world show that the proposed hybrid centrality called k-core neighborhood density (KND) remarkably balances rank distribution and accuracy, and the identified influential nodes have a superior ability to spread their influence over a wide area of a network.
Spectral Efficiency Centrality (SEC), a temporal spectral centrality framework that quantifies node importance by evaluating the change in spectral radius caused by node removal across temporal snapshots, and ASEC, an efficient approximation based on Perron-Frobenius theory and first-order eigenvalue perturbation that offers a computationally efficient solution for large-scale temporal networks.
Time-evolving networks, or temporal networks, play a crucial role in modeling dynamic interactions across various domains, including biology, social sciences, and information technology. Unlike static networks, these systems undergo continuous changes in topology and edge weights, influencing processes such as information flow, transportation efficiency, and neural activity. Understanding and controlling these networks are essential for predicting future behavior and optimizing dynamic processes. This work focuses on the problem of dynamic centrality, a measure of node importance in time-dependent networks. Specifically, we address how to steer network centrality to a desired state by making minimal modifications to the network structure. This problem is formulated as an optimal control problem for an ordinary differential equation, either matrix- or vector-based, where the control acts on network edges. The proposed framework generalizes centrality control problems studied in static networks and leverages the Pontryagin Maximum Principle for efficient solutions. For large-scale problems, the required matrix-function actions are approximated by Krylov-type techniques, avoiding the explicit formation of dense matrix functions. Numerical experiments on synthetic and real temporal networks show that the proposed framework can effectively steer receive centrality under prescribed control constraints.
Temporal higher-order networks, where each hyperlink involving a group of nodes is activated or deactivated over time, effectively represent social interactions. They serve as substrates for the spread of epidemics and information. However, the contribution of each hyperlink to a contagion process, namely, the average number of nodes that are infected via its activation, and the network properties of hyperlinks that influence this contribution, remain unexplored. Here we show, for the Susceptible-Infectious threshold process on temporal higher-order networks derived from human face-to-face interactions, that the contribution of each hyperlink can be quantified by a contagion backbone, whose dependency on the diffusion parameters is demonstrated and supported by theoretical analysis. We design centrality metrics of hyperlinks to estimate hyperlink rankings based on their contributions, revealing that local properties of hyperlinks can effectively identify high-contributing hyperlinks, and explain why different centrality metrics perform better under different process parameters. These insights are crucial for designing effective interventions that mitigate the spread of epidemics or misinformation.