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Link Prediction in Heterogeneous Collaboration Networks

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Social Network Analysis - Community Detection and Evolution

Part of the book series: Lecture Notes in Social Networks ((LNSN))

Abstract

Traditional link prediction techniques primarily focus on the effect of potential linkages on the local network neighborhood or the paths between nodes. In this article, we study both supervised and unsupervised link prediction in networks where instances can simultaneously belong to multiple communities, engendering different types of collaborations. Links in these networks arise from heterogeneous causes, limiting the performance of predictors that treat all links homogeneously. To solve this problem, we introduce a new supervised link prediction framework, Link Prediction using Social Features (LPSF), which incorporates a reweighting scheme for the network based on nodes’ features extracted from patterns of prominent interactions across the network. Experiments on coauthorship networks demonstrate that the choice for measuring link weights can be critical for the link prediction task. Our proposed reweighting method in LPSF better expresses the intrinsic relationship between nodes and improves prediction accuracy for supervised link prediction techniques. We also compare the unsupervised performance of the individual features used within LPSF with two new diffusion-based methods: Link Prediction using Diffusion Process (LPDP) and Link Prediction using Diffusion Maps (LPDM). Experiments demonstrate that LPDP is able to identify similar node pairs, even far away ones, that are connected by weak ties in the coauthorship network using the diffusion process; however, reweighting the network has little impact on prediction performance.

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Notes

  1. 1.

    http://www.informatik.uni-trier.de/~ley/db/.

References

  1. Adamic L, Adar E (2003) Friends and neighbors on the web. Soc Netw 25(3):211–230

    Article  Google Scholar 

  2. Ahn YY, Bagrow JP, Lehmann S (2010) Link communities reveal multi-scale complexity in networks. Nature 466:761–764

    Article  Google Scholar 

  3. Backstrom L, Leskovec J (2011) Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the fourth ACM international conference on web search and data mining, pp 635–644

    Google Scholar 

  4. Barla A, Odone F, Verr A (2003) Histogram intersection kernel for image classification. In: Proceedings 2003 international conference on image processing, vol 3, III-513-16

    Google Scholar 

  5. Benchettara N, Kanawati R, Rouveirol C (2010) Supervised machine learning applied to link prediction in bipartite social networks. In: Proceedings of the international conference on advances in social network analysis and mining, pp 326–330

    Google Scholar 

  6. Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 30(1–7):107–117

    Article  Google Scholar 

  7. Coifman RR, Lafon S (2006) Diffusion maps. Appl Comput Harmon Anal 21(1):5–30

    Article  MATH  MathSciNet  Google Scholar 

  8. Davis D, Lichtenwalter R, Chawla NV (2012) Supervised methods for multi-relational link prediction. Social network analysis and mining, pp 1–15

    Google Scholar 

  9. de Sá HR, Prudêncio RBC (2011) Supervised link prediction in weighted networks. In: International joint conference on neural networks (IJCNN), pp 2281–2288

    Google Scholar 

  10. Ding Y (2011) Applying weighted pagerank to author citation networks. CoRR abs/1102.1760

  11. Donoser M, Bischof H (2013) Diffusion processes for retrieval revisited. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), pp 1320–1327

    Google Scholar 

  12. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor Newsl 11(1):10–18

    Article  Google Scholar 

  13. Hasan MA, Chaoji V, Salem S, Zaki M (2006) Link prediction using supervised learning. In: Proceedings of the SDM workshop on link analysis, counterterrorism and security

    Google Scholar 

  14. Jin EM, Girvan M, Newman MEJ (2001) The structure of growing social networks. Phys Rev E 64:046132

    Article  Google Scholar 

  15. Kong X, Shi X, Yu PS (2011) Multi-label collective classification. In: SIAM international conference on data mining (SDM), pp 618–629

    Google Scholar 

  16. Lee JB, Adorna H (2012) Link prediction in a modified heterogeneous bibliographic network. In: Proceedings of international conference on advances in social networks analysis and mining (ASONAM), pp 442–449

    Google Scholar 

  17. Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inf Sci Technol 58(7):1019–1031

    Article  Google Scholar 

  18. Lichtenwalter RN, Lussier JT, Chawla NV (2010) New perspectives and methods in link prediction. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, pp 243–252

    Google Scholar 

  19. Liu W, Lu L (2010) Link prediction based on local random walk. EPL (Europhys Lett) 85(5)

    Google Scholar 

  20. Liu J, Yang Y, Shah M (2009) Learning semantic visual vocabularies using diffusion distance. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), pp 461–468

    Google Scholar 

  21. Lu L, Zhou T (2011) Link prediction in complex networks: a survey. Phys A 390(6):1150–1170

    Article  MathSciNet  Google Scholar 

  22. Lü L, Zhou T (2009) Role of weak ties in link prediction of complex networks. In: Proceedings of the ACM international workshop on complex networks meet information and knowledge management, pp 55–58

    Google Scholar 

  23. Murata T, Moriyasu S (2007) Link prediction of social networks based on weighted proximity measures. In: Web intelligence, pp 85–88

    Google Scholar 

  24. Newman M (2001) Clustering and preferential attachment in growing networks. Phys Rev E 64(2):025102

    Article  Google Scholar 

  25. Newman MEJ (2004) Detecting community structure in networks. Eur Phys J B - Condens Matter Complex Syst 38(2):321–330

    Article  Google Scholar 

  26. Ou Q, Jin YD, Zhou T, Wang BH, Yin BQ (2007) Power-law strength-degree correlation from resource-allocation dynamics on weighted networks. Phys Rev E 75:021102

    Article  Google Scholar 

  27. Pan JY, Yang HJ, Faloutsos C, Duygulu P (2004) Automatic multimedia cross-modal correlation discovery. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, pp 653–658

    Google Scholar 

  28. Popescul A, Popescul R, Ungar LH (2003) Statistical relational learning for link prediction. In: IJCAI workshop on learning statistical models from relational data

    Google Scholar 

  29. Pujari M, Kanawati R (2012) Tag recommendation by link prediction based on supervised machine learning. In: Proceedings of the international conference on weblogs and social media

    Google Scholar 

  30. Salton G, McGill MJ (1986) Introduction to modern information retrieval. McGraw-Hill Inc, New York

    Google Scholar 

  31. Sen P, Namata G, Bilgic M, Getoor L, Gallagher B, Eliassi-Rad T (2008) Collective classification in network data. AI Mag 29:93–106

    Google Scholar 

  32. Soundarajan S, Hopcroft J (2012) Using community information to improve the precision of link prediction methods. In: Proceedings of the international conference on the world wide web, pp 607–608

    Google Scholar 

  33. Sun Y, Barber R, Gupta M, Aggarwal CC, Han J (2011) Co-author relationship prediction in heterogeneous bibliographic networks. In: Proceedings of the international conference on advances in social networks analysis and mining, pp 121–128

    Google Scholar 

  34. Tang L, Liu H (2009) Scalable learning of collective behavior based on sparse social dimensions. In: Proceedings of international conference on information and knowledge management (CIKM)

    Google Scholar 

  35. Taskar B, Wong MF, Abbeel P, Koller D (2003) Link prediction in relational data. In: Neural information processing systems

    Google Scholar 

  36. Wanga J, Lia Y, Baib X, Zhanga Y, Wangc C, Tang N (2011) Learning context-sensitive similarity by shortest path propagation. Pattern Recognit 44(10–11):2367–2374

    Article  Google Scholar 

  37. Wang X, Sukthankar G (2011) Extracting social dimensions using Fiedler embedding. In: Proceedings of IEEE international conference on social computing, pp 824–829

    Google Scholar 

  38. Wang X, Sukthankar G (2013) Link prediction in multi-relational collaboration networks. In: Proceedings of the IEEE/ACM International conference on advances in social networks analysis and mining. Niagara Falls, Canada, pp 1445–1447

    Google Scholar 

  39. Xiang EW (2008) A survey on link prediction models for social network data. Sci technol

    Google Scholar 

  40. Yang X, Koknar-Tezel S, Latecki LJ (2009) Locally constrained diffusion process on locally densified distance spaces with applications to shape retrieval. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR)

    Google Scholar 

  41. Yin Z, Gupta M, Weninger T, Han J (2010) A unified framework for link recommendation using random walks. In: 2010 international conference on advances in social networks analysis and mining (ASONAM), pp 152–159

    Google Scholar 

  42. Zhou T, Lü L, Zhang YC (2009) Predicting missing links via local information. Eur Phys J B - Condens Matter Complex Syst 71(4):623–630

    Article  MATH  Google Scholar 

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Acknowledgments

This research was supported in part by NSF IIS-08451.

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Authors and Affiliations

  1. Department of EECS, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL, 32816, USA

    Xi Wang & Gita Sukthankar

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  1. Xi Wang
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  2. Gita Sukthankar
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Correspondence to Gita Sukthankar .

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Editors and Affiliations

  1. Département d'Informatique et Ingénirie, Université du Québec en Outaouais, Gatineau, Québec, Canada

    Rokia Missaoui

  2. Département de Mathématiques et Informatique, Université Cheikh Anta Diop, Dakar, Senegal

    Idrissa Sarr

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Wang, X., Sukthankar, G. (2014). Link Prediction in Heterogeneous Collaboration Networks. In: Missaoui, R., Sarr, I. (eds) Social Network Analysis - Community Detection and Evolution. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-12188-8_8

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  • DOI: https://doi.org/10.1007/978-3-319-12188-8_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12187-1

  • Online ISBN: 978-3-319-12188-8

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