The Local Triangle Structure Centrality Method to Rank Nodes in Networks. (2nd January 2019)

• Record Type: Journal Article
• Authors: Ma, Xiaojian; Ma, Yinghong
• Other Names: Volchenkov Dimitri Academic Editor.
• Is Part Of: Complexity. Volume 2019(2019)
• Publication Date: 2019-01-02
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2019/9057194
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : Detecting influential spreaders had become a challenging and crucial topic so far due to its practical application in many areas, such as information propagation inhibition and disease dissemination control. Some traditional local based evaluation methods had given many discussions on ranking important nodes. In this paper, ranking nodes of networks continues to be discussed. A semilocal structures method for ranking nodes based on the degree and the neighbors' connections of the node is presented. The semilocal structures are regarded as the number of neighbors of the nodes and the connections between the node and its neighbors. We combined the triangle structure and the degree information of the neighbors to define the inner-outer spreading ability of the nodes and then summed the node neighbors' inner-outer spreading ability to be used as the local triangle structure centrality (LTSC). The LTSC avoids the defect "pseudo denser connections" in measuring the structure of neighbors. The performance of the proposed LTSC method is evaluated by comparing the spreading ability on both real-world and synthetic networks with the SIR model. The simulation results of the discriminability and the correctness compared with pairs of ranks (one is generated by SIR model and the others are generated by central nodes measures) show that LTSC outperforms some other local or semilocal methods in evaluating the node's influence in most cases, such as degree, betweenness, H-index, local centrality, local structure centrality, K-shell, and S-shell. The experiments prove that the LTSC is an efficient and accurate ranking method which provides a more reasonable evaluating index to rank nodes than some previous approaches.