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2009 | Vol. 38, no 1 | 67-86
Tytuł artykułu

Evaluation of node position based on email communication

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Rapid development of various kinds of social networks within the Internet enabled investigation of their properties and analyzing their structure. An interesting scientific problem in this domain is the assessment of the node position within the directed, weighted graph that represents the social network of email users. The new method of node position analysis, which takes into account both the node positions of the neighbors and the strength of connections between network nodes, is presented in the paper. The node position can be used to discover key network users, who are the most important in the population and who have potentially the greatest influence on others. The experiments carried out on two datasets enabled studying the main properties of the new measure.

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Bibliogr. 30 poz., rys.
  • Wrocław University of Technology, Institute of Informatics, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland
  • ADAMIC, L.A. and ADAR, E. (2003) Friends and Neighbors on the Web. Social Networks 25 (3), 211-230.
  • BAVELAS, A. (1950) Communication patterns in task - oriented groups. Journal of the Acoustical Society of America 22, 271-282.
  • BERKHIN, A. (2005) A Survey on PageRank Computing. Internet Mathematics 2 (1), 73-120.
  • BOTAFOGO, R.A., RIVLIN, E. and SHNEIDERMAN, B. (1992) Structural analysis of hypertexts: identifying hierarchies and useful metrics. ACM Transaction on Information Systems 10 (2), 142-180.
  • BOYD, D.M. (2004) Friendster and Publicly Articulated Social Networking. Conference on Human Factors and Computing Systems (CHI 2004). ACM Press, 1279-1282.
  • BRIM, S and PAGE, L. (1998) The Anatomy of a Large-Scale Hypertextual Web Search Engine. Corny. Networks and ISDN Syst. 30 (1-7), 107-117.
  • BRINKMEIER, M. (2006) PageRank Revisited. ACM Transactions on Internet Technology 6 (3), 282-301.
  • CULOTTA, A., BEKKERMAN, R. and McCALLUM, A. (2004) Extracting social networks and contact information from email and the Web. CEAS 2004, First Conference on Email and Anti-Spam,
  • FLAKE, G.W., LAWRENCE, S. and GILES, C.L. (2000) Efficient identification of Web communities. Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM Press, 150-160.
  • FREEMAN, L.C. (1979) Centrality in social networks: Conceptual clarification. Social Networks I (3), 215-239.
  • GARTON, L., HAYTHORNTWAITE, C. and WELLMAN, B. (1997) Studying Online Social Networks. J. of Computer-Mediated Communication 3, 1.
  • GIBSON, D., KLEINBERG, J. and RAGHAVAN, P. (1998) Inferring Web communities from link topology. Proc. of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space-structure in hypermedia systems: links, objects, time and space-structure in hypermedia systems. ACM Press, New York, 225-234.
  • GOLBECK, J. (2005) Computing and Applying Trust in Web-Based Social Networks. Dissertation Submitted to the Faculty of the Graduate School of the Universtity of Maryland.
  • GOLBECK, J. and HENDLER, J.A. (2004) Accuracy of Metrics for Inferring Trust and Reputation in Semantic Web-Based Social Networks. Engineering Knowledge in the Age of the Semantic Web, 14th International Conference, EKAW 2004. LNCS 3257, Springer Verlag, 116-131.
  • HANNEMAN, R. and RIDDLE, M. (2006) Introduction to social network methods. Online textbook, available from: /nettext/.
  • KATZ, L. (1953) A new status derived from sociometrics analysis. Psychometrica 18, 39-43.
  • KAZIENKO, P. and ADAMSKI, M. (2007) AdROSA - Adaptive Personalization of Web Advertising. Information Sciences 177 (11), 2269-2295.
  • KAZIENKO, P. and MUSIAŁ, K. (2006) Recommendation Framework for Online Social Networks. Studies in Computational Intelligence. Advances in Web Intelligence and Data Mining. Springer Verlag, 23, 111-120.
  • KAZIENKO, P. and MUSIAŁ, K. (2007) On Utilizing Social Networks to Discover Representatives of Human Communities. International Journal of Intelligent Information and Database Systems 1 (3/4), 293-310.
  • KAZIENKO, P., MUSIAŁ, K. and ZGRZYWA, A. (2007) Evaluation of Node Position Based on Mutual Interaction in Social Network of Internet Users. II Krajowa Konferencja Naukowa Technologie Przetwarzania Danych, TPD 2007. Wydawnictwo Politechniki Poznańskiej, 265-276.
  • KAZIENKO, P. and MUSIAŁ, K. (2008) Mining Personal Social Features in the Community of Email Users. SOFSEM 2008: Theory and Practice of Computer Science. 34th Conference on Current Trends in Theory and Practice of Computer Science, Novy Smokovec, Slovakia. LNCS 4910, Springer Verlag, 708-719.
  • KENDALL, M.G. (1948) Rank Correlation Methods. Charles Griffin & Company Ltd., London.
  • MUSIAŁ K., KAZIENKO, P. and KAJDANOWICZ, T. (2008) Multirelational Social Networks in Multimedia Sharing Systems. In: N.T. Nguyen, G. Kołaczek and B. Gabrys, eds., Knowledge Processing and Reasoning for Information Society. Academic Publishing House EXIT, Warsaw, 275-292.
  • PRIEBEY, C.E., CONROY, J.M., MARCHETTE, D.J. and PARK, Y. (2005) Scan Statistics on Enron Graphs. Computational & Mathematical Organization Theory 11 (3), 229-247.
  • RANA, O.F. and HINZE, A. (2004) Trust and reputation in dynamic scientific communities. IEEE Distributed Systems Online 5 (1).
  • SHETTY, J. and ADIBI, J. (2005) Discovering Important Nodes through Graph Entropy The Case of Enron Email Databases. 3rd International Workshop on Link Discovery. ACM Press, 74-81.
  • VALVERDE, S., THERAULAZ, G., GAUTRAIS, J., FOURCASSIE, V. and SOLE, R.V. (2006) Self-organization patterns in wasp and open source communities. IEEE Intelligent Systems 21 (2), 36-40.
  • WASSERMAN, S. and FAUST, K. (1994) Social Network Analysis: Methods and Applications. Cambridge University Press, New York.
  • WELLMAN, B. and SALAFF, J. (1996) Computer Networks as Social Networks: Collaborative Work, Telework, and Virtual Community. Annual Review Social 22, 213-238.
  • YANG, W.S., DIA, J.B., CHENG, H.C. and LIN, H.T. (2006) Mining Social Networks for Targeted Advertising. HICSS-39, IEEE Comp. Soc., 137a.
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