Abstract
In research on complex networks, mining relatively important nodes is a challenging and practical work. However, little research has been done on mining relatively important nodes in complex networks, and the existing relatively important node mining algorithms cannot take into account the indicators of both precision and applicability. Aiming at the scarcity of relatively important node mining algorithms and the limitations of existing algorithms, this paper proposes a relatively important node mining method based on distance distribution and multi-index fusion (DDMF). First, the distance distribution of each node is generated according to the shortest path between nodes in the network; then, the cosine similarity, Euclidean distance and relative entropy are fused, and the entropy weight method is used to calculate the weights of different indexes; Finally, by calculating the relative importance score of nodes in the network, the relatively important nodes are mined. Through verification and analysis on real network datasets in different fields, the results show that the DDMF method outperforms other relatively important node mining algorithms in precision, recall, and AUC value.
Highlights
With the vigorous growth of network and information technology represented by the Internet, human society has entered a new and complex era of networks
A relatively important node mining method based on distance distribution and multi-index fusion (DDMF) is proposed
Relative Importance Measure Based on Distance Distribution and Multi-index Fusion
Summary
With the vigorous growth of network and information technology represented by the Internet, human society has entered a new and complex era of networks. Determining which nodes are the most important in the network relative to one or one group of specific nodes presents an issue This problem reminds us about the practical significance of mining relatively important information in networks, especially very large-scale ones. Mining relatively important nodes in complex networks obviously offers great research significance and application value [15]. A relatively important node mining method based on distance distribution and multi-index fusion (DDMF) is proposed. The DDMF method involves two main steps: First, the distance distribution of all nodes (including known important nodes and target nodes) is calculated on the basis of the shortest distance between nodes in the network. The DDMF method fills the gap of relatively important node algorithms in the scientific field of complex network theory, and provides a new idea for community detection and link prediction.
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