Identification of influential nodes in complex networks: A local degree dimension approach

The importance of researches on complex networks is becoming more and more promi-nent. How to identify influential nodes is still an urgent and crucial issue of many researches on complex networks. Many centrality measures, each has its own emphasis, have been put forward by researchers. Among them, centrality measures based on local properties of nodes are widely used, which assess the importance of nodes based on their degrees. However, they do not take the global information of networks into consideration. In this paper, a Local Degree Dimension (LDD) approach to identify influential nodes in complex networks is proposed. Different from the existing work, LDD regards the numbers of central node's each layer neighbor nodes as the basis of nodes' importance calculation. LDD creatively combines the increasing rate and decreasing rate of the numbers of each layer neighbor nodes to obtain its Local Degree Dimension value, which is comprehensive and reasonable. A node with a larger LDD value has a more significant impact on networks. To demonstrate the effectiveness of LDD, six experiments are conducted on six real-world complex networks. Experimental results show that LDD has a higher identification accu-racy and a stronger ability to quantify node's importance. (c) 2022 Elsevier Inc. All rights reserved.

Automated summary

The importance of research on complex networks is increasing, and identifying influential nodes remains an urgent and crucial issue. This paper proposes a Local Degree Dimension (LDD) approach that assesses the importance of nodes in complex networks by considering the increasing and decreasing rates of the numbers of each layer neighbor nodes. Experimental results demonstrate the effectiveness of LDD in accurately identifying influential nodes and quantifying their importance.