The Structure Entropy-Based Node Importance Ranking Method for Graph Data

Due to its wide application across many disciplines, how to make an efficient ranking for nodes in graph data has become an urgent topic. It is well-known that most classical methods only consider the local structure information of nodes, but ignore the global structure information of graph data. In order to further explore the influence of structure information on node importance, this paper designs a structure entropy-based node importance ranking method. Firstly, the target node and its associated edges are removed from the initial graph data. Next, the structure entropy of graph data can be constructed by considering the local and global structure information at the same time, in which case all nodes can be ranked. The effectiveness of the proposed method was tested by comparing it with five benchmark methods. The experimental results show that the structure entropy-based node importance ranking method performs well on eight real-world datasets.

Automated summary

Due to its wide application across many disciplines, an efficient ranking method for nodes in graph data has become an urgent topic. Most classical methods only consider the local structure information of nodes and ignore the global structure information. This paper proposes a structure entropy-based method to rank node importance by considering both local and global structure information. Experimental results on eight real-world datasets demonstrate the effectiveness of the proposed method.