An Improved Social Network Analysis Method for Social Networks
Recently, Social Network Service (SNS) users are rapidly increasing, and Social Network Analysis (SNA) methods are used to analyze the structure of user relationship or messages in many fields. However, the SNA methods based on the shortest distance among
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Abstract Recently, Social Network Service (SNS) users are rapidly increasing, and Social Network Analysis (SNA) methods are used to analyze the structure of user relationship or messages in many fields. However, the SNA methods based on the shortest distance among nodes is time-consuming in measuring computation time. In order to solve this problem, we present a heuristic method for the shortest path search using SNS user graphs. Our proposed method consists of three steps. First, it sets a start node and a goal node in the Social Network (SN), which is represented by trees. Second, the goal node sets a temporary node starting from a skewed tree, if there is a goal node on a leaf node of the skewed tree. Finally, the betweenness and closeness centralities are computed with the heuristic shortest path search. For verification of the proposed method, we demonstrate an experimental analysis of betweenness centrality and closeness centrality, with 164,910 real data in an SNS. In the experimental results, the method shows that the computation time of betweenness centrality and closeness centrality is faster than the traditional method. This heuristic method can be used to analyze social phenomena and trends in many fields.
J. Sohn (&) Service Strategy Team, Visual Display, Samsung Electronics, Suwon, South Korea e-mail: [email protected] D. Kang H. Park I.-J. Chung Department of Computer and Information Science, Korea University, Seoul, South Korea e-mail: [email protected] H. Park e-mail: [email protected] I.-J. Chung e-mail: [email protected] B.-G. Joo Department of Computer and Information Communications, Hongik University, Seoul, South Korea e-mail: [email protected]
Y.-M. Huang et al. (eds.), Advanced Technologies, Embedded and Multimedia for Human-centric Computing, Lecture Notes in Electrical Engineering 260, DOI: 10.1007/978-94-007-7262-5_13, Springer Science+Business Media Dordrecht 2014
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Keywords Social network (SN) Social network analysis (SNA) centrality Closeness centrality Heuristic approach
Betweenness
Introduction Recently, online social network services are becoming popular with users, along with the expansion of Web 2.0-based services and the widespread use of smart devices. Online SNSs are online community services which enables users to communicate with each other, share information, and expand their human relationships [1]. In an SNS, each relation between users is represented by a simple graph, which consists of nodes and edges. As online SNS users are increasing rapidly, SNSs are actively utilized in enterprise marketing, analysis of social phenomena, trends, and so forth [2, 3]. Meanwhile, SNA is a way of analyzing social relationships among users in an SN. Through the SNA, it is possible to measure relationships between members, degree of intimacy, and intensity of connection, and to detect communities. The following are conventional SNA methods: degree centrality, betweenness centrality, and closeness centrality [4]. In the deg
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