Abstract
Contrary to previous static knowledge, our dynamic view in social network is so limited. Recent uncovering those hidden dynamic patterns has posed a series of challenging problems in network evolution. To make effective exploration, we present a fundamentally novel framework for uncovering the intricate properties of evolutionary networks. Different from static snapshots methods, we firstly trace the timelines of networks, which could explicitly characterize the network to several evolving segments. Then based on extracted smooth segments from the timeline, a graph approximation algorithm is devised to capture the frequent characteristics of the network and reduce the noise of interactions. Moreover, by employing the relationship between multi-attributes, an innovative community detection algorithm is proposed for detailed analysis on the approximate graphs. Besides the algorithms, to track these dynamic communities, we also introduce a community correlation and evaluation criterion. Finally, applying this framework to several synthetic and real-world datasets, we demonstrate the critical relationship between event and social evolution, and find that close-knit relationship with well-distributed tie strengths among members of large communities will contribute to a longer life span.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Tang, L., Liu, H., Zhang, J.P., Nazeri, Z.: Community Evolution in Dyanmic Multi-Mode Networks. In: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 677–685 (2008)
Leskovec, J., Backstrom, L., Kumar, R., Tomkins, A.: Microscopic Evolution of Social Networks. In: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 462–470 (2008)
Lin, Y.R., Chi, Y., Zhu, S.H., Sundaram, H., Tseng, B.L.: Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. In: Proceeding of the 17th international conference on World Wide Web, pp. 685–694 (2008)
Onnela, J.P., Saramaki, J., Hyvonen, J., Szabo, G., Lazer, D., Kaski, K., Kertesz, J., Barabasi, A.L.: Structure and tie strengths in mobile communication networks. In: Proceedings of the National Academy of Sciences, vol. 104, pp. 7332–7336 (2007)
Sun, J., Faloutsos, C., Papadimitriou, S., Yu, P.S.: GraphScope: Parameter-free Mining of Large Time-evolving Graphs. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 687–696 (2007)
Asur, S., Parthasarathy, S., Ucar, D.: An event-based framework for characterizing the evolutionary behavior of interaction graphs. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 913–921 (2007)
Palla, G., Barabasi, A.-L., Vicsek, T.: Quantifying social group evolution. Nature 446, 664–667 (2007)
Long, B., Xu, X.Y., Zhang, Z.F., Yu, P.S.: Community Learning by Graph Approximation. In: Proceedings of the 2007 Seventh IEEE International Conference on Data Mining, pp. 232–241 (2007)
Tanya, Y., Berger-Wolf, Jared, Saia.: A Framework for Analysis of Dynamic Social Networks. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 523–528 (2006)
Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.Y.: Group Formation in Large Social Networks: Membership, Growth and Evolution. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 44–54 (2006)
Kumar, R., Novak, J., Tomkins, A.: Structure and evolution of online social networks. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 611–617 (2006)
Derényi, I., Palla, G., Vicsek, T.: Clique percolation in random networks. Phys. Rev. Lett. 29, 94(16), 160–202 (2005)
Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. EÂ 69(6), 066133 (2004)
Bollinger, J.A.: Bollinger on Bollinger Bands, 1st edn. McGraw-Hill, New York (2001)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99(12), 7821–7826 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Yang, S., Wu, B., Wang, B. (2009). Tracking the Evolution in Social Network: Methods and Results. In: Zhou, J. (eds) Complex Sciences. Complex 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02466-5_67
Download citation
DOI: https://doi.org/10.1007/978-3-642-02466-5_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02465-8
Online ISBN: 978-3-642-02466-5
eBook Packages: Computer ScienceComputer Science (R0)