Abstract
Identifying vital nodes in complex networks is of paramount importance in understanding and controlling the spreading dynamics. Currently, this study is facing great challenges in dealing with big data in many real-life applications. With the deepening of the research, scholars began to realize that the analysis on traditional graph model is insufficient because many nodes in a multilayer network share connections among different layers. To address this problem both efficiently and effectively, a novel algorithm for identifying vital nodes in both monolayer and multilayer networks is proposed in this paper. Firstly, a node influence measure is employed to determine the initial leader of a local community. Subsequently, the community structures are revealed via the Maximum Influential Neighbors Expansion (MINE) strategy. Afterward, the communities are regarded as super-nodes for an iteratively folding process till convergence, in order to identify influencers hierarchically. Numerical experiments on 32 real-world datasets are conducted to verify the performance of the proposed algorithm, which shows superiority to the competitors. Furthermore, we apply the proposed algorithm in the graph of adjacencies derived from the maps of China and USA. The comparison and analysis of the identified provinces (or states) suggest that the proposed algorithm is feasible and reasonable on real-life applications.
Highlights
The rapid development of information technology has witnessed a blossom of network science in the last decade
Identifying vital nodes in complex networks is crucial in understanding and controlling the spreading dynamics, and it can be further split into two processes: (1) ranking of vital nodes and (2) identification of a small group of nodes that are able to maximize the influence
Mathematics 2020, 8, 1449 in identifying the vital notes within complex networks, summarized as the following: (1) “Volume”, the huge data scale has become prohibitive in tracking with the full data set, which may require the power of high-performance computing or distributed processing; (2) “Variety”, many real-life complex systems are naturally heterogeneous networks, i.e., a more complicated structure that includes multiple interactions among various channels, which may lead to traditional research on a single channel insufficient to get integrated results; (3) “Velocity”, the dynamic changes of networks force an efficient analysis to satisfy the real-time graph mining; (4) “Value”, the big data implies great practical application value and requires an outstanding performance for the proposed algorithms
Summary
The rapid development of information technology has witnessed a blossom of network science in the last decade. Mathematics 2020, 8, 1449 in identifying the vital notes within complex networks, summarized as the following: (1) “Volume”, the huge data scale has become prohibitive in tracking with the full data set, which may require the power of high-performance computing or distributed processing; (2) “Variety”, many real-life complex systems are naturally heterogeneous networks, i.e., a more complicated structure that includes multiple interactions among various channels, which may lead to traditional research on a single channel insufficient to get integrated results; (3) “Velocity”, the dynamic changes of networks force an efficient analysis to satisfy the real-time graph mining; (4) “Value”, the big data implies great practical application value and requires an outstanding performance for the proposed algorithms. (1) A novel algorithm for identifying vital nodes hierarchically is proposed in this paper, in which community structures derived by maximum influential neighbors expansion strategy are regarded as higher-level nodes.
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