Evaluating importance of nodes in complex networks with local volume information dimension
This work addresses node importance evaluation for researchers in network science, but it appears incremental as it builds on existing methods with a more comprehensive approach.
The paper tackles the problem of evaluating node importance in complex networks by proposing a new approach called local volume information dimension, which calculates the sum of degrees within different distances and uses information entropy, and experiments on real-world networks show promising results.
How to evaluate the importance of nodes is essential in research of complex network. There are many methods proposed for solving this problem, but they still have room to be improved. In this paper, a new approach called local volume information dimension is proposed. In this method, the sum of degree of nodes within different distances of central node is calculated. The information within the certain distance is described by the information entropy. Compared to other methods, the proposed method considers the information of the nodes from different distances more comprehensively. For the purpose of showing the effectiveness of the proposed method, experiments on real-world networks are implemented. Promising results indicate the effectiveness of the proposed method.