Abstract
As the popularity of Web 2.0 increases, web users used to establish communication relationships and share resources with others online. Recently, one important research for retrieving information from the web has become a major issue. Moreover, due to the social network popularity, there is much more opportunity to find out various human resources to help people get what they want. In our work, we propose to help the social network users to find out the most reachable experts who are authorized researchers on a common topic area with the users. For this purpose, we propose how to calculate centrality closeness from requesting user to experts, analyze the relevancy of an expert to the topic, and finally combine both of the result to rank the most reachable experts for the requesting user.
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References
Girvan, M., Newman, M.E.: Community Structure in Social and Biological Networks. Proceedings of National Acad. Sciences, U.S.A (2002)
Das, S., Egecioglu, O., El Abbado, A.: Anonymizing Weighted Social Network Graphs. In: Proceedings of 2010 IEEE 26th International Conference on Data Engineering (2010)
Jun, T., Yu, L., Peng, Y.: A Trust Discovery Model Based on Weighted Closeness. In: Proceedings of the 10th International Symposium on Pervasive Systems, Algorithms, and Networks, ISPAN (2009)
Rosen, Z., Griffiths, M., Steyvers, M., Smyth, P.: The Author-topic Model for Authors and Documents. In: Proceedings of the 20th UAI Conference (2004)
Seid, D., Kobsa, A.: Expert Finding Systems for Organization: Problem and Domain Analysis and the DEMOIR Approach. Journal of Organizational Computing and Electronic Commerce 13(1) (2003)
Arnetminer, http://www.arnetminer.net
Jie, T., Jing, Z.: ArnetMiner: Extraction and Mining of Academic Social Networks. In: Proceedings of KDD, Las Vegas, Nevada, USA (2008)
Campbell, C., Maglio, P., Cozzi, A., Dom, B.: Expertise Identification using Email Communications. In: Proceedings of Twelfth International Conference on Information and Knowledge Management. ACM Press, New York (2003)
Fu, Y., Xiang, R., Liu, Y., Zhang, M., Ma, S.: Finding Experts using Social Network Analysis. In: Proceedings of Web Intelligence, IEEE/WIC/ACM International Conference (2007)
Li, J.Z., Jie, T., Jing, Z.: EOS: Expertise Oriented Search Using Social Network. In: Proceedings of the 16th International Conference on World Wide Web (2007)
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Jin, L., Yoon, J.Y., Kim, Y.H., Kim, U.M. (2012). Based on Analyzing Closeness and Authority for Ranking Expert in Social Network. In: Huang, DS., Gan, Y., Gupta, P., Gromiha, M.M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2011. Lecture Notes in Computer Science(), vol 6839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25944-9_36
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DOI: https://doi.org/10.1007/978-3-642-25944-9_36
Publisher Name: Springer, Berlin, Heidelberg
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