Abstract:

In view of the fact that most researches on multilayer hypernetwork mainly focus on the topology structure， and influence node identification methods involve relatively single indicators， which cannot comprehensively and accurately identify influence nodes， an identification method of influence nodes in multilayer hypernetwork based on evidence theory was proposed. Firstly， based on the topology structure of multilayer hypernetwork， Multilayer Aggregation Hypernetwork （MAH） was constructed according to the idea of aggregation network. Secondly， the discernment framework of problem was defined based on evidence theory. Finally， Dempster-Shafer （D-S） evidence combination method was used to fuse local， location and global indicators of network to identify influence nodes. The proposed method was applied to physics-computer science double-layer scientific research cooperation hypernetwork constructed by arXiv dataset. Compared with hyperdegree centrality， K-shell， closeness centrality methods， etc.， the proposed method has the fastest propagation speed and reaches steady state first in the Susceptible-Infected-Susceptible （SIS） hypernetwork propagation model based on Reactive Process （RP） and Contact Process （CP） strategies. After isolating top 6% of influence nodes， the average network hyperdegree， clustering coefficient and network efficiency decreased. With the increase of proportion of isolated influence nodes， the growth rate of number of network subgraphs was similar to that of the closeness centrality method. The coarse granularity of identification result was measured by monotonicity index value， which reached 0.999 8， and recognition result had a high discrimination degree. The results of several experiments show that the proposed identification method of influence nodes in multilayer hypernetwork is accurate and effective.

Key words: evidence theory, multilayer hypernetwork, discernment framework, evidence combination, influence node, hypernetwork propagation

摘要：

针对多层超网络研究多集中于拓扑结构，且影响力节点识别方法中涉及指标较为单一，无法全面准确识别影响力节点的情况，提出一种基于证据理论的多层超网络影响力节点识别方法。首先，在多层超网络拓扑结构基础上，根据聚合网络思想构建多层聚合超网络；其次，基于证据理论定义问题的辨识框架；最后，利用D-S（Dempster-Shafer）证据组合方法，融合网络的局部、位置和全局指标以识别网络影响力节点。将该方法应用于arXiv数据集构建的物理-计算机科学双层科研合作超网络（MAH），在基于RP（Reactive Process）和CP（Contact Process）策略的易感-感染-易感（SIS）超网络传播模型中，与超度中心性、K-shell、接近中心性方法等相比，传播速度最快，且最先达到稳态；隔离影响力排名前6%节点后，网络平均超度、聚类系数以及网络效率均减小；随着隔离影响力节点比例的增大，网络子图数量增速与接近中心性方法相近；通过单调性指标值度量识别结果粗粒度，达到0.999 8，识别结果具有较高区分度。综合多个实验结果，表明该多层超网络影响力节点识别方法准确有效。

关键词: 证据理论, 多层超网络, 辨识框架, 证据组合, 影响力节点, 超网络传播