Mining Algorithm of Relatively Important Nodes Based on Edge Importance Greedy Strategy

Abstract

Relatively important node mining has always been an essential research topic in complex networks. Existing relatively important node mining algorithms suffer from high time complexity and poor accuracy. Therefore, this paper proposes an algorithm for mining relatively important nodes based on the edge importance greedy strategy (EG). This method considers the importance of the edge to represent the degree of association between two connected nodes. Therefore, the greater the value of the connection between a node and a known important node, the more likely it is to be an important node. If the importance of the edges in an undirected network is measured, a greedy strategy can find important nodes. Compared with other relatively important node mining methods on real network data sets, such as SARS and 9/11, the experimental results show that the EG algorithm excels in both accuracy and applicability, which makes it a competitive algorithm in the mining of important nodes in a network.

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

“…The neighbors that can be reached by the short route are assigned the larger weight. At the same time, the neighbors that can be reached by the long route are assigned the small weight [ 17 ].…”

The Self-Information Weighting-Based Node Importance Ranking Method for Graph Data

“…Although the accuracy of rank results can be improved with the help of entropy, most of these methods only consider the mutual influence between nodes and ignore the influence of edges that directly connected to the node itself [ 32 , 33 , 34 ]. Certainly, as the important component of graph data, the information contained in the edges can make a huge influence for the final ranking [ 35 ]. Therefore, how to measure the amount of information contained in the edges and make full use of them is vital.…”