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International Conference on Discovery Science

DS 2013: Discovery Science pp 324–339Cite as

Hyperlink Prediction in Hypernetworks Using Latent Social Features

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNAI,volume 8140)

Abstract

Predicting the existence of links between pairwise objects in networks is a key problem in the study of social networks. However, relationships among objects are often more complex than simple pairwise relations. By restricting attention to dyads, it is possible that information valuable for many learning tasks can be lost. The hypernetwork relaxes the assumption that only two nodes can participate in a link, permitting instead an arbitrary number of nodes to participate in so-called hyperlinks or hyperedges, which is a more natural representation for complex, multi-party relations. However, the hyperlink prediction problem has yet to be studied. In this paper, we propose HPLSF (Hyperlink Prediction using Latent Social Features), a hyperlink prediction algorithm for hypernetworks. By exploiting the homophily property of social networks, HPLSF explores social features for hyperlink prediction. To handle the problem that social features are not always observable, a latent social feature learning scheme is developed. To cope with the arbitrary cardinality hyperlink issue in hypernetworks, we design a feature-embedding scheme to map the a priori arbitrarily-sized feature set associated with each hyperlink into a uniformly-sized auxiliary space. To address the fact that observed features and latent features may be not independent, we generalize a structural SVM to learn using both observed features and latent features. In experiments, we evaluate the proposed HPLSF framework on three large-scale hypernetwork datasets. Our results on the three diverse datasets demonstrate the effectiveness of the HPLSF algorithm. Although developed in the context of social networks, HPLSF is a general methodology and applies to arbitrary hypernetworks.

Keywords

  • Hypernetworks
  • Hyperlink Prediction
  • Social Features

Dan Rockmore was partially supported by AFOSR Award FA9550-11-1-0166 and the Neukom Institute for Computational Science at Dartmouth College. Ye Xu was partially supported by a grant from the Alfred P. Sloan Foundation.

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  • DOI: 10.1007/978-3-642-40897-7_22
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References

  1. Backstrom, L., Leskovec, J.: Supervised random walks: predicting and recommending links in social networks. In: WSDM 2011 (2011)

    Google Scholar 

  2. Barros, R.C., Basgalupp, M.P., de Carvalho, A., Freitas, A.A.: A survey of evolutionary algorithms for decision-tree induction. IEEE Trans. SMC 42(3), 291–312 (2012)

    Google Scholar 

  3. Bautu, E., Kim, S., Bautu, A., Luchian, H., Zhang, B.-T.: Evolving hypernetwork models of binary time series for forecasting price movements on stock markets. In: IEEE Evolutionary Computation 2009 (2009)

    Google Scholar 

  4. Bichot, C.-E., Siarry, P.: Graph Partitioning: Optimisation and Applications. Wiley (2011)

    Google Scholar 

  5. Bonachich, P., Holdren, A., Johnston, M.: Hyper-edges and multidimensional centrality. Social Networks 26(3), 189–203 (2004)

    CrossRef  Google Scholar 

  6. Cox, T.F., Cox, M.A.A.: Multidimensional Scaling. Chapman and Hall (2001)

    Google Scholar 

  7. Gloor, P.A., et al.: Towards growing a coin in a medical research community. Procedia Social and Behavioral Sciences (2010)

    Google Scholar 

  8. Gao, S., Denoyer, L., Gallinari, P.: Temporal link prediction by integrating content and structure information. In: CIKM 2011 (2011)

    Google Scholar 

  9. Grangier, D., Melvin, I.: Feature set embedding for incomplete data. In: NIPS 2010 (2010)

    Google Scholar 

  10. Ha, J.-W., Eom, J.-H., Kim, S.-C., Zhang, B.-T.: Evolutionary hypernetwork models for aptamer-based cardiovascular disease diagnosis. In: GECCO 2007 (2007)

    Google Scholar 

  11. Hoff, P.D.: Modeling homophily and stochastic equivalence in relational data. In: NIPS 2007 (2007)

    Google Scholar 

  12. Hoff, P.D., Raftery, A.E., Handcock, M.S.: Latent space approaches to social network analysis. J. American Statistical Association 97, 1090–1098 (2001)

    MathSciNet  CrossRef  Google Scholar 

  13. Hopcroft, J., Khan, O., Kulis, B., Selman, B.: Natural communities in large linked networks. In: SIGKDD 2003 (2003)

    Google Scholar 

  14. Joachims, T., Finley, T., Yu, C.J.: Cutting-plane training of structural svm. Machine Learning 77(1), 27–59 (2009)

    CrossRef  MATH  Google Scholar 

  15. Kang, F., Jin, R., Sukthankar, R.: Correlated label propagation with application to multi-label learning. In: CVPR 2006 (2006)

    Google Scholar 

  16. Kim, S., Kim, S.-J., Zhang, B.-T.: Evolving hypernetwork classifiers for microrna expression profile analysis. In: IEEE Evolutionary Computation 2007 (2007)

    Google Scholar 

  17. Kleinbaum, A.M.: Organizational misfits and the origins of brokerage in intra-firm networks. Administrative Science Quarterly 57, 407–452 (2012)

    CrossRef  Google Scholar 

  18. Kleinbaum, A.M., Stuart, T.E.: Inside the black box of the corporate staff: Social networks and the implementation of corporate strategy. Strategic Management Journal (2013)

    Google Scholar 

  19. Kondor, R., Jebara, T.: A kernel between set of vectors. In: ICML 2003 (2003)

    Google Scholar 

  20. Kossinets, G., Watts, D.J.: Empirical analysis of an evolving social network. Science (2006)

    Google Scholar 

  21. Liben-Nowell, D., Kleinberg, J.: The link prediction problem for social networks. In: CIKM 2003 (2003)

    Google Scholar 

  22. Lichtenwalter, R.N., Lussier, J.T., Chawla, N.V.: New perspectives and methods in link prediction. In: SIGKDD 2010 (2010)

    Google Scholar 

  23. Macskassy, S.A., Provost, F.: Classification in networked data: A toolkit and a univariate case study. JMLR 8, 935–983 (2007)

    Google Scholar 

  24. McFee, B., Lanckriet, G.: Metric learning to rank. In: ICML 2010 (2010)

    Google Scholar 

  25. McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: Homophily in social networks. Annual Review of Sociology 27(1), 415–444 (2001)

    CrossRef  Google Scholar 

  26. Miller, K.T.: Bayesian nonparametric latent feature models. Ph.D. Thesis, University of California, Berkeley (2011)

    Google Scholar 

  27. Miller, K.T., Griffiths, T.L., Jordan, M.I.: Nonparametric latent feature models for link prediction. In: NIPS 2009 (2009)

    Google Scholar 

  28. Neville, J., Jensen, D.: Leveraging relational autocorrelation with latent group models. In: SIGKDD Workshop 2005 (2005)

    Google Scholar 

  29. Newman, M.: The structure and function of complex networks. SIAM Review 45(1), 167–256 (2003)

    MathSciNet  CrossRef  MATH  Google Scholar 

  30. Newman, M.E.J.: Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103(23), 8577–8582 (2006)

    CrossRef  Google Scholar 

  31. Palla, G., Derenyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005)

    CrossRef  Google Scholar 

  32. Pothen, A.: Graph partitioning algorithms with applications to scientific computing. Technical Report, Norfolk, VA (1997)

    Google Scholar 

  33. Shi, J., Malik, J.: Normalized cuts and image segmentation. TPAMI 22(8), 888–905 (2000)

    CrossRef  Google Scholar 

  34. Sun, L., Ji, S., Ye, J.: Hypergraph spectral learning for multi-label classification. In: SIGKDD 2008 (2008)

    Google Scholar 

  35. Tang, L., Liu, H.: Relational learning via latent social dimensions. In: SIGKDD 2009 (2009)

    Google Scholar 

  36. Tsochantaridis, I., Hofmann, T., Joachims, T., Altun, Y.: Support vector learning for interdependent and structured output spaces. In: ICML 2004 (2004)

    Google Scholar 

  37. Wang, C., Satuluri, V., Parthasarathy, S.: Local probabilistic models for link prediction. In: IEEE ICDM 2007 (2007)

    Google Scholar 

  38. Xie, L., Gu, N., Li, D., Cao, Z., Tan, M., Nahavandi, S.: Concurrent control chart patterns recognition with singular spectrum analysis and support vector machine. Computers and Industry Engineering 64(1), 280–289 (2013)

    CrossRef  Google Scholar 

  39. Xie, L., Li, D., Simske, S.J.: Feature dimensionality reduction for example-based image super-resolution. Journal of Pattern Recognition Research 2, 130–139 (2011)

    CrossRef  Google Scholar 

  40. Xu, Y., Ping, W., Campbell, A.: Multi-instance metric learning. In: ICDM 2011 (2011)

    Google Scholar 

  41. Xu, Y., Rockmore, D.: Feature selection for link prediction. In: PIKM 2012 (2012)

    Google Scholar 

  42. Zhou, D., Huang, J., Schölkopf, B.: Learning with hypergraphs: Clustering, classification, and embedding. In: NIPS 2006 (2006)

    Google Scholar 

  43. Zhu, J.: Max-margin nonparametric latent feature models for link prediction. In: ICML 2012 (2012)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Science Department, Dartmouth College, USA

    Ye Xu & Dan Rockmore

  2. Department of Maths, Dartmouth College, USA

    Dan Rockmore

  3. The Santa Fe Institute, USA

    Dan Rockmore

  4. Tuck School of Business, Dartmouth College, USA

    Adam M. Kleinbaum

Authors
  1. Ye Xu
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  2. Dan Rockmore
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  3. Adam M. Kleinbaum
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Editor information

Editors and Affiliations

  1. TU Darmstadt, Germany

    Johannes Fürnkranz

  2. Phillips-Universität Marburg, Germany

    Eyke Hüllermeier

  3. The Institute of Statistical Mathematics, Tokyo, Japan

    Tomoyuki Higuchi

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Xu, Y., Rockmore, D., Kleinbaum, A.M. (2013). Hyperlink Prediction in Hypernetworks Using Latent Social Features. In: Fürnkranz, J., Hüllermeier, E., Higuchi, T. (eds) Discovery Science. DS 2013. Lecture Notes in Computer Science(), vol 8140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40897-7_22

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  • DOI: https://doi.org/10.1007/978-3-642-40897-7_22

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