Importance Recognition of Nodes in Hypernetworks Based on Multi-indicator Decision Matrix
ZHU Fuqia,b, JIA Xiaoyana,b, WEI Liangb,c, LI Faxua,b
a. College of Computer; b. The State Key Laboratory of Tibetan Intelligent Information Processing and Application; c. Academy of Fine Arts, Qinghai Normal University, Xining 810008, China
Abstract：Aiming at the problems that the methods for recognizing important nodes in hypernetworks ignore the influence of hyperedges on nodes and the evaluation indexes are relatively single, a method of node importance identification based on multi-indicator decision matrix is proposed. The method characterizes the local importance of node using the hyperdegree, considering the effect of hyperedges on nodes, the vector subgraph centrality is defined to portray the diffusion ability of nodes, and measures the global influence of node by reflecting its positional information through betweenness centrality, the contribution weights of each metric are determined based on entropy theory to assess the importance of the node in terms of its own influence and that of the associated hyperedges. Through the experimental validation of monotonicity, robustness, and SIR propagation model in different types of hypernetworks, the results show that the method can recognize the important nodes in the network more accurately and effectively.
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