TFRank: An Evaluation of Users Importance with Fractal Views in Social Networks
One of research issues in social network analysis is to
evaluate the position/importance of users in social networks. As the
information diffusion in social network is evolving, it seems difficult
to evaluate the importance of users using traditional approaches. In
this paper, we propose an evaluation approach for user importance
with fractal view in social networks. In this approach, the global importance
(Fractal Importance) and the local importance (Topological
Importance) of nodes are considered. The basic idea is that the bigger
the product of fractal importance and topological importance of a
node is, the more important of the node is. We devise the algorithm
called TFRank corresponding to the proposed approach. Finally, we
evaluate TFRank by experiments. Experimental results demonstrate
our TFRank has the high correlations with PageRank algorithm
and potential ranking algorithm, and it shows the effectiveness and
advantages of our approach.
[1] N. He, W. Y. Gan, D. Y. Li, Evaluate nodes importance in the network
using data field thoery, 2007 International Conference on Convergence
Information Technology, pp.1225-1230, 2007.
[2] Freeman L. C. Centrality in social networks: I. Conceptual clarification,
Social Networks, 1, pp.215-239, 1979.
[3] K. Musial, P. Kazienko, P. Brodka, User position measures in social
networks, The 3rd SNA-KDD Workshop, 2009.
[4] S. Brin and L. Page, The anatomy of a large-scale hypertextual web search
engine, Computer Networks, 30, pp.107-117, 1998.
[5] Jon M. Kleinberg. Authoritative sources in a hyperlinked environment.
Journal of the ACM, 46(5), 1999.
[6] Y. S. Han, L. Y Kim, J. W. Cha, Evaluation of user reputation on
YouTube, LNCS 5621, pp.346-353, 2009.
[7] C. C. Yang, M. Sageman, Analysis of terrorist social networks with fractal
views, Journal of Information Science, 35(3), pp.299-320, 2009.
[8] H. Koike, Fractal views: a fractal-based method for controlling information
display, ACM Transactions on Information Systems 13(3), 1995.
[9] H. Koike and H. Yoshihara, Fractal approaches for visualizing huge
hierarchies. In: Proceedings of IEEE Symposium on Visual Laugnagues,
1993.
[10] J. Xu and H. Chen, Fighting organized crimes: using shortest-path
algorithms to identify associations in criminal networks, Decision Support
Systems, 38, pp.473-487, 2003.
[1] N. He, W. Y. Gan, D. Y. Li, Evaluate nodes importance in the network
using data field thoery, 2007 International Conference on Convergence
Information Technology, pp.1225-1230, 2007.
[2] Freeman L. C. Centrality in social networks: I. Conceptual clarification,
Social Networks, 1, pp.215-239, 1979.
[3] K. Musial, P. Kazienko, P. Brodka, User position measures in social
networks, The 3rd SNA-KDD Workshop, 2009.
[4] S. Brin and L. Page, The anatomy of a large-scale hypertextual web search
engine, Computer Networks, 30, pp.107-117, 1998.
[5] Jon M. Kleinberg. Authoritative sources in a hyperlinked environment.
Journal of the ACM, 46(5), 1999.
[6] Y. S. Han, L. Y Kim, J. W. Cha, Evaluation of user reputation on
YouTube, LNCS 5621, pp.346-353, 2009.
[7] C. C. Yang, M. Sageman, Analysis of terrorist social networks with fractal
views, Journal of Information Science, 35(3), pp.299-320, 2009.
[8] H. Koike, Fractal views: a fractal-based method for controlling information
display, ACM Transactions on Information Systems 13(3), 1995.
[9] H. Koike and H. Yoshihara, Fractal approaches for visualizing huge
hierarchies. In: Proceedings of IEEE Symposium on Visual Laugnagues,
1993.
[10] J. Xu and H. Chen, Fighting organized crimes: using shortest-path
algorithms to identify associations in criminal networks, Decision Support
Systems, 38, pp.473-487, 2003.
@article{"International Journal of Information, Control and Computer Sciences:56886", author = "Fei Hao and Hai Wang", title = "TFRank: An Evaluation of Users Importance with Fractal Views in Social Networks", abstract = "One of research issues in social network analysis is to
evaluate the position/importance of users in social networks. As the
information diffusion in social network is evolving, it seems difficult
to evaluate the importance of users using traditional approaches. In
this paper, we propose an evaluation approach for user importance
with fractal view in social networks. In this approach, the global importance
(Fractal Importance) and the local importance (Topological
Importance) of nodes are considered. The basic idea is that the bigger
the product of fractal importance and topological importance of a
node is, the more important of the node is. We devise the algorithm
called TFRank corresponding to the proposed approach. Finally, we
evaluate TFRank by experiments. Experimental results demonstrate
our TFRank has the high correlations with PageRank algorithm
and potential ranking algorithm, and it shows the effectiveness and
advantages of our approach.", keywords = "TFRank, Fractal Importance, Topological Importance,
Social Network", volume = "5", number = "12", pages = "1630-6", }