Abstract
Identifying vital nodes is a basic problem in social network research. The existing theoretical framework mainly focuses on the lower-order structure of node-based and edge-based relations and often ignores important factors such as interactivity and transitivity between multiple nodes. To identify the vital nodes more accurately, a high-order structure, named as the motif, is introduced in this paper as the basic unit to evaluate the similarity among the node in the complex network. It proposes a notion of high-order degree of nodes in complex network and fused the effect of the high-order structure and the lower-order structure of nodes, using evidence theory to determine the vital nodes more efficiently and accurately. The algorithm was evaluated from the function of network structure. And the SIR model was adopted to examine the spreading influence of the nodes ranked. The results of experiments in different datasets demonstrate that the algorithm designed can identify vital nodes in the social network accurately.
Supported by the Natural Science Foundation of China (No. 61662066, 61163010).
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Zhang, M., Yan, G., Wang, Y., Lv, Y. (2020). Identifying Vital Nodes in Social Networks Using an Evidential Methodology Combining with High-Order Analysis. In: Zeng, J., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1257. Springer, Singapore. https://doi.org/10.1007/978-981-15-7981-3_8
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