注意力流网络中节点影响力的层级性研究

复杂网络中节点影响力的层级性在网络结构与控制研究中至关重要。针对有向加权网络中节点影响力的层级性问题，基于海量在线用户行为数据，构建有向加权集体注意力流网络。通过定义节点的层级位置时间和位置约束指标，并结合节点的拓扑位置和时间序列，提出一种用于有向加权网络的节点影响力度量及排序算法。实验结果表明，该算法能有效区分网络层级结构，准确识别出最具影响力的节点，对于节点影响力评估与复杂网络可控性研究具有一定的借鉴意义和参考价值。

Research on the Hierarchy of Node Influence in Attention Flow Network

The hierarchy of node influence in complex networks is crucial for the research on network structure and controllability. As for the problem about the hierarchy of node influence in directed and weighted networks, a directed and weighted collective attention flow network is constructed based on massive data of online user behavior. By defining the Hierarchical Position Time(HPT) and position constraint of nodes, an algorithm for measuring and ranking the node influence in directed weighted networks is proposed. The algorithm also considers the topological positions and time series of nodes. Experimental results show that this algorithm can distinguish the hierarchical structure of network, identifying the most influential nodes accurately. It displays certain reference significance and reference value in evaluating the influence of nodes and the controllability of complex networks.