Jul 2026· 2026 8th International Conference on Electronics and Communication, Network and Computer Technology (ECNCT)· pp. 304-308· 0 citations· 14 references
Abstract
Accurate identification of key nodes in dynamic temporal networks is critical to network propagation control and governance. To address the issues that traditional temporal multilayer networks insufficiently consider node heterogeneity and inadequate inter-layer information fusion, this paper proposes the MSAM (Supra-Adjacency Matrix based on Multi-order Neighborhood Structure) model for node importance identification based on inter-layer multi-order structure similarity. The model characterizes intra-layer attributes via K-shell decomposition and the Pareto principle, measures inter-layer similarity by integrating multi-order neighbor and hierarchical fluctuation information, and realizes heterogeneous node coupling modeling. Experiments on real-world datasets demonstrate that MSAM achieves significantly better ranking performance than baseline models. It exhibits strong robustness and generalization ability under different propagation intensities, effectively improving the accuracy of node importance identification in dynamic networks.
Highlights This paper mainly adopts hypernetwork modeling to characterize the higher-order coupling of Spatial Information Networks, proposes a critical node identification algorithm based on KL NMF soft community partitioning and interweaving degree to achieve critical node identification under multi-service collabora...
Xiao-Lan Yu, Wei Xiong, Ping Jian et al.· Italian National Conference...· 0 citations
A model for identifying influence nodes in multilayer networks that leverages multilayer feature fusion, encompassing intra-layer features, weighted centrality features, and inter-layer structural features is proposed, which outperforms classical and heuristic baselines in terms of resilience, generalization, and robus...
Shristi Achari, R. Beniwal, Sanjay Kumar· International Journal of Mod...· 0 citations
Accurate assessment of node importance in complex networks is crucial for enhancing network robustness and security, for instance, by protecting critical data nodes or reinforcing core hubs. However, existing importance metrics often struggle to simultaneously accommodate local connectivity, neighborhood influence an...
Peng-Cheng Cai, Yi Xie, Qi-Chen Wang et al.· Scientific Reports· 0 citations
The existing key node identification methods in complex networks rely on local topological information or global structural features, which are difficult to fully integrate multi-order local structural information and global propagation characteristics, resulting in the limitation of the accuracy and discrimination abi...
Katz centrality is a well-established measure to identify and rank the most important nodes in complex networks by means of a linear system solve. Recent works have developed notions of Katz centrality for temporal, i.e., time-evolving networks. Their drawback is that small changes in the network structure may drastica...
Kai Bergermann, Francesco Gravili, V. Simoncini et al.· 0 citations
Understanding information propagation mechanisms in large-scale heterogeneous networks requires effective representation of complex relational structures and diffusion dynamics. This study proposes a graph neural network (GNN)-based framework for topology-aware identification of structural communities and propagation p...
