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Identifying important nodes for temporal networks based on the ASAM model
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NSTL
Elsevier
The identification of important nodes in a temporal network is of great significance for the analysis and control of the information dissemination process. In this work, the multi-layer coupled network analysis method is employed to identify important nodes in a temporal network. First, to overcome the problem of a fixed constant being unable to reflect differences in the inter-layer coupling relationship, and by combining a node's own neighbors and common neighbors of nodes in two-time layers, a new Enhanced Similarity Index (ESI) is proposed to measure the inter-layer coupling relationship. Secondly, the attenuation factor is introduced to more accurately describe the inter-layer coupling relationship. Finally, an Attenuation-Based Supra-Adjacency Matrix (ASAM) temporal network modeling method based on the attenuation of the inter-layer coupling strength is proposed. The importance of nodes in the temporal network is evaluated by calculating the eigenvector centrality of the nodes in each time layer in the temporal network. It is found that after deleting a certain percentage of the important nodes identified by the ASAM method, the temporal Largest Connected Component (LCC) of the network becomes smaller, and the network performance is improved as compared with the SAM and SSAM methods. The results indicate that the important nodes identified by the ASAM are at the core of the network and have a greater impact on the network structure and functions. This demonstrates that the proposed ASAM model can more effectively identify important nodes in the temporal network, and has significant application value in this research field. (C) 2021 Elsevier B.V. All rights reserved.
Temporal networkAttenuation factorEigenvector-based centralityTemporal largest connected componentInter-layer coupling relationship
NSTL
Elsevier
