Abstract
In the field of complex networks, identifying important nodes is of great importance both in theoretical and practical applications. Compared with the important node identification of the static network, the important node identification of the temporal network is a more urgent problem to solve since most complex networks in reality change with time. The degree centrality method in static networks shows that the more nodes a node is connected to, that is, the more nodes it has in its neighborhood, the more influential and important this node is. Inspired by this method, our idea is that in a temporal network, as time changes, if a node’s neighborhood keeps adding new nodes, the more nodes it affects, the more important it is. Therefore, we propose a new method for identifying important nodes in temporal networks, namely the temporal neighborhood change centrality(TNCC). The TNCC of a node is equal to its average neighborhood change rate over a period of time. The larger the TNCC of a node, the more important it is. We evaluate the proposed method against 7 baseline methods on 6 real temporal networks based on the infectious disease model SIR. Experimental results show that our method is more stable in identifying important nodes and has advantages in most cases.
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Wu, Z., He, L., Tao, L., Wang, Y., Zhang, Z. (2023). Temporal Neighborhood Change Centrality for Important Node Identification in Temporal Networks. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13623. Springer, Cham. https://doi.org/10.1007/978-3-031-30105-6_38
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DOI: https://doi.org/10.1007/978-3-031-30105-6_38
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