Abstract
Identifying influential nodes in complex networks has attracted the attention of many researchers in recent years. However, due to the high time complexity, methods based on global attributes have become unsuitable for large-scale complex networks. In addition, compared with methods considering only a single attribute, considering multiple attributes can enhance the performance of the method used. Therefore, this paper proposes a new multiple local attributes-weighted centrality (LWC) based on information entropy, combining degree and clustering coefficient; both one-step and two-step neighborhood information are considered for evaluating the influence of nodes and identifying influential nodes in complex networks. Firstly, the influence of a node in a complex network is divided into direct influence and indirect influence. The degree and clustering coefficient are selected as direct influence measures. Secondly, based on the two direct influence measures, we define two indirect influence measures: two-hop degree and two-hop clustering coefficient. Then, the information entropy is used to weight the above four influence measures, and the LWC of each node is obtained by calculating the weighted sum of these measures. Finally, all the nodes are ranked based on the value of the LWC, and the influential nodes can be identified. The proposed LWC method is applied to identify influential nodes in four real-world networks and is compared with five well-known methods. The experimental results demonstrate the good performance of the proposed method on discrimination capability and accuracy.
Highlights
Many systems in the real world can be modeled as complex networks to facilitate the study of the systems by complex network analysis
Inspired by the above discussion, we propose a new multi-attribute weighted centrality based on information entropy, combining degree and clustering coefficient; both one-step and two-step neighborhood information are considered
In the following part of this paper, new centrality measures based on the degree and clustering coefficient are proposed, and some of these centrality measures will be used as comparison methods for experiments
Summary
Many systems in the real world can be modeled as complex networks to facilitate the study of the systems by complex network analysis. Mo and Deng [38] adopted D-S evidence theory to comprehensively consider degree centrality, betweenness centrality, efficiency centrality, and correlation centrality for evaluating the importance of nodes in complex networks These multi-attributes methods have proved to be promising strategies to evaluate the influence of nodes. Inspired by the above discussion, we propose a new multi-attribute weighted centrality based on information entropy, combining degree and clustering coefficient; both one-step and two-step neighborhood information are considered. This paper proposes a new multi-attribute weighted centrality with a low computational complexity O(n), as it is calculated based on four local attributes, including degree, two-hop degree, clustering coefficient, and two-hop clustering coefficient. In the following part of this paper, new centrality measures based on the degree and clustering coefficient are proposed, and some of these centrality measures will be used as comparison methods for experiments. J∈N(i) where N(i) is the set of the one-hop neighbors of node i, cci is the clustering coefficient of node i, which can be calculated as Formula (6), and ki refers to the degree of node i
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