Abstract
The widespread use of social networks enables the rapid diffusion of information, e.g., news, among users in very large communities. It is a substantial challenge to be able to observe and understand such diffusion processes, which may be modeled as networks that are both large and dynamic. A key tool in this regard is data summarization. However, few existing studies aim to summarize graphs/networks for dynamics. Dynamic networks raise new challenges not found in static settings, including time sensitivity and the needs for online interestingness evaluation and summary traceability, which render existing techniques inapplicable. We study the topic of dynamic network summarization: how to summarize dynamic networks with millions of nodes by only capturing the few most interesting nodes or edges over time, and we address the problem by finding interestingness-driven diffusion processes. Based on the concepts of diffusion radius and scope, we define interestingness measures for dynamic networks, and we propose OSNet, an online summarization framework for dynamic networks. We report on extensive experiments with both synthetic and real-life data. The study offers insight into the effectiveness and design properties of OSNet.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Boldi, P., Vigna, S.: The webgraph framework I: compression techniques. In: WWW, pp. 595–602 (2004)
Cha, M., Mislove, A., Gummadi, K.P.: A measurement-driven analysis of information propagation in the Flickr social network. In: WWW, pp. 721–730 (2009)
Chakrabarti, D., Faloutsos, C.: Graph Mining: Laws, Tools, and Case Studies. Morgan & Claypool Publishers (2012)
Desmier, E., Plantevit, M., Robardet, C., Boulicaut, J.-F.: Trend mining in dynamic attributed graphs. In: ECML/PKDD, pp. 654–669 (2013)
Ferlež, J., Faloutsos, C., Leskovec, J., Mladenic, D., Grobelnik, M.: Monitoring network evolution using MDL. In: ICDE, pp. 1328–1330 (2008)
Hage, C., Jensen, C.S., Pedersen, T.B., Speicys, L., Timko, I.: Integrated data management for mobile services in the real world. In: VLDB, pp. 1019–1030 (2003)
Inokuchi, A., Washio, T., Motoda, H.: An apriori-based algorithm for mining frequent substructures from graph data. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 13–23. Springer, Heidelberg (2000)
Kumar, R., Novak, J., Tomkins, A.: Structure and evolution of online social networks. In: SIGKDD, pp. 611–617 (2006)
Leskovec, J., McGlohon, M., Faloutsos, C., Glance, N.S., Hurst, M.: Patterns of cascading behavior in large blog graphs. In: SDM, pp. 551–556 (2007)
Lin, Y.-R., Sundaram, H., Kelliher, A.: Summarization of social activity over time: people, actions and concepts in dynamic networks. In: CIKM, pp. 1379–1380 (2008)
Liu, S., Liu, Y., Ni, L.M., Fan, J., Li, M.: Towards mobility-based clustering. In: SIGKDD, pp. 919–928 (2010)
Liu, S., Wang, S., Zhu, F., Zhang, J., Krishnan, R.: HYDRA: Large-scale social identity linkage via heterogeneous behavior modeling. In: SIGMOD Conference, pp. 51–62 (2014)
Liu, S., Yue, Y., Krishnan, R.: Adaptive collective routing using gaussian process dynamic congestion models. In: SIGKDD, pp. 704–712 (2013)
Liu, W., Kan, A., Chan, J., Bailey, J., Leckie, C., Pei, J., Kotagiri, R.: On compressing weighted time-evolving graphs. In: CIKM, pp. 2319–2322 (2012)
Navlakha, S., Rastogi, R., Shrivastava, N.: Graph summarization with bounded error. In: SIGMOD Conference, pp. 419–432 (2008)
Qu, Q., Zhu, F., Yan, X., Han, J., Yu, P.S., Li, H.: Efficient topological OLAP on information networks. In: Yu, J.X., Kim, M.H., Unland, R. (eds.) DASFAA 2011, Part I. LNCS, vol. 6587, pp. 389–403. Springer, Heidelberg (2011)
Raghavan, S., Garcia-molina, H.: Representing web graphs. In: ICDE, pp. 405–416 (2003)
Sun, J., Faloutsos, C., Papadimitriou, S., Yu, P.S.: GraphScope: parameter-free mining of large time-evolving graphs. In: SIGKDD, pp. 687–696 (2007)
Tian, Y., Hankins, R.A., Patel, J.M.: Efficient aggregation for graph summarization. In: SIGMOD Conference, pp. 567–580 (2008)
Toivonen, H., Zhou, F., Hartikainen, A., Hinkka, A.: Compression of weighted graphs. In: SIGKDD, pp. 965–973 (2011)
Xie, R., Zhu, F., Ma, H., Xie, W., Lin, C.: CLEar: A real-time online observatory for bursty and viral events. PVLDB 7(11) (2014)
Yang, J., Counts, S.: Predicting the speed, scale, and range of information diffusion in Twitter. In: ICWSM, pp. 355–358 (2010)
Zhu, F., Qu, Q., Lo, D., Yan, X., Han, J., Yu, P.S.: Mining top-k large structural patterns in a massive network. PVLDB 4(11), 807–818 (2011)
Zhu, F., Zhang, Z., Qu, Q.: A direct mining approach to efficient constrained graph pattern discovery. In: SIGMOD Conference, pp. 821–832 (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Qu, Q., Liu, S., Jensen, C.S., Zhu, F., Faloutsos, C. (2014). Interestingness-Driven Diffusion Process Summarization in Dynamic Networks. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2014. Lecture Notes in Computer Science(), vol 8725. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44851-9_38
Download citation
DOI: https://doi.org/10.1007/978-3-662-44851-9_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44850-2
Online ISBN: 978-3-662-44851-9
eBook Packages: Computer ScienceComputer Science (R0)