KEYWORDS: Stars, Scientific research, Physics, Social network analysis, Analytical research, Detection and tracking algorithms, Reflection, Lithium, Information science, Decision making
Based on the articles published on journal Physical Review E (PRE), the influence of academic groups is evaluated by social networks analysis and the multi-attribute decision making method TOPSIS. At first, co-citing network established by considering the citing relationship among articles, and then the academic groups are detected by using community detection algorithms on co-cited network. After that, the index system for evaluating the influence of academic group is proposed, and finally a multi-attribute decision making method TOPSIS is used to evaluate the influence. The proposed method provides a new insight into analyzing the influences of academic groups, and can comprehensively describe academic groups’ influence.
The detection of clusters is benefit for understanding the organizations and functions of networks. Clusters, or communities, are usually groups of nodes densely interconnected but sparsely linked with any other clusters. To identify communities, an efficient and effective community agglomerative algorithm based on node similarity is proposed. The proposed method initially calculates similarities between each pair of nodes, and form pre-partitions according to the principle that each node is in the same community as its most similar neighbor. After that, check each partition whether it satisfies community criterion. For the pre-partitions who do not satisfy, incorporate them with others that having the biggest attraction until there are no changes. To measure the attraction ability of a partition, we propose an attraction index that based on the linked node’s importance in networks. Therefore, our proposed method can better exploit the nodes’ properties and network’s structure. To test the performance of our algorithm, both synthetic and empirical networks ranging in different scales are tested. Simulation results show that the proposed algorithm can obtain superior clustering results compared with six other widely used community detection algorithms.
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