OTHER RELATED PUBLICATIONS

    2011

  • N. Chiluka, N. Andrade and J. Pouwelse. A Link Prediction Approach to Recommendations in Large-Scale User-Generated Content Systems. Lecture Notes in Computer Science, 6611 (2011) 189.
  • A. Narayanan, E. Shi, and B. Rubinstein. Link prediction by De-anonymization: How We Won the Kaggle Social Network Challenge. In Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN).
  • L. Backstrom and J. Leskovec. Supervised random walks: predicting and recommending links in social networks. In WSDM’11 Proceedings of the fourth ACM international conference on web and data mining, New York, 2011.
  • S.-H. Yang, B. Long, A. Smola, N. Sadagopan, Z. Zheng, and H Zha. Like like alike: joint friendship and interest propagation in social networks. In WWW '11 Proceedings of the 20th international conference on World wide web ACM New York, NY 2011.
  • L. Munasinghe and R. Ichis. Time Aware Index for Link Prediction in Social Networks. Lecture Notes in Computer Science, 6862 (2011) 342.
  • C. H. Nguyen and H. Mamitsuka. Kernels for Link Prediction with Latent Feature Models. Lecture Notes in Computer Science, 6912 (2011) 517.
  • S. Scellato, A. Noulas, C. Mascolo. Exploiting Place Features in Link Prediction on Location-Based Social Networks. In Proceeding of KDD’11, San Diego, California, USA, 2011.
  • X. Feng, J. Zhao, and K. Xu. Link prediction in Complex Networks: A Clustering Perspective. In Proceedings of CoRR. 2011.
  • M. A. Hasan and M. J. Zaki. A Survey of Link Prediction in Social Networks. Social Networks Data Analytics, 2011, pp:243-275.
  • X. Qi. Bridging link and query intent to enhance web search. In HT’11 Proceedings of the 22nd ACM conference Hypertext and hypermedia, NewYork, 2011.
  • D. M. Dumlavy, T. G. Kolda, and E. Acar. Temporal Link Prediction Using Matrix and Tensor Factorizations. In ACM Transactions on Knowledge Discovery from Data, 5 (2011) 10:1-27.
  • 2010

  • J. Kunegis, E.W. De Luca, and S. Albayrak. The link prediction problem in bipartite networks. In Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty (IPMU'10), Springer-Verlag, Berlin, Heidelberg, 2010, pp: 380-389.
  • M. Sachan and R. Ichise. Using Semantic Information to Improve Link Prediction Results in Network Datasets. IACSIT International Journal of Engineering and Technology, Vol.2, No.4, August 2010, ISSN: 1793-8236.
  • R. N. Lichtenwalter, J. T. Lussier, and N. V. Chawla. New Perspectives and Methods in Link Prediction. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '10). ACM, New York, NY, USA, 2010, pp: 243-252.
  • V. Leroy, B.B. Cambazoglu, and F. Bonchi. Cold start link prediction. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, New York, 2010, p. 393.
  • J. R. Doppa, J. Yu, P. Tadepalli, and L. Getoor. Learning Algorithms for Link Prediction based on Chance Constraints. In Proceedings of ECML/PKDD (1)'2010. pp.344~360.
  • B. Cao, N.N. Liu, and Q. Yang. Transfer learning for collective link prediction in multiple heterogenous domains. In Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel
  • J. Leskovec, D. Huttenlocher, and J. Kleinberg. Predicting positive and negative links in online social networks. In Proceedings of WWW’ 2010, ACM Press, New York, 2010.
  • 2009

  • E. Acar, D. M. Dunlavy, and T. G. Kolda. Link Prediction on Evolving Data Using Matrix and Tensor Factorizations. In Proceedings of the 2009 IEEE International Conference on Data Mining Workshops (ICDMW '09). IEEE Computer Society, Washington, DC, USA, pp: 262-269.
  • X. Li, H. Chen. Recommendation as link prediction: a graph kernel-based machine learning approach. In Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries (JCDL '09). ACM, New York, NY, USA, 2009, pp: 213-216.
  • H. H. Song, T. W. Cho, V. Dave, Y. Zhang, and L. Qiu. Scalable proximity Estimation and Link Prediction in Online Social Networks. In Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference (IMC '09). ACM, New York, NY, USA, 2009, pp: 322-335.
  • J. Kunegis and A. Lommatzsch. Learning Spectral Graph Transformations for Link Prediction. In Proceeding ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning. 2009, pp: 561-568.
  • J. Doppa, J. Yu, P. Tadepalli, and L. Getoor. Chance-Constrained Programs for Link Prediction. In Proceedings of Workshop on Analyzing Networks and Learning with Graphs at NIPS Conference, 2009.
  • P. D. Hoff. Multiplicative latent factor models for description and prediction of social networks. Computational & Mathematical Organization Theory, 15 (2009) 261.
  • Z. Huang and D.K.J. Lin. The time-series link prediction problem with applications in communication surveillance. INFORMS J. Comput. 21 (2009) 286.
  • T. Tylenda, R. Angelova, and S. Bedathur. Towards time-aware link prediction in evolving social networks. In Proceedings of the 3rdWorkshop on Social Network Mining and Analysis, ACM Press, New York,
  • J. Kunegis, A. Lommatzsch, and C. Bauckhage. The slashdot zoo: mining a social network with negative edges. In Proceedings of WWW’ 2009, ACM Press, New York, 2009.
  • R. Guimerà and M. Sales-Pardo. Missing and spurious interactions and the reconstruction of complex networks. Proc. Natl. Acad. Sci. USA 106 (2009) 22073.
  • 2008

  • B. Gallagher, H. Tong, T. Eliassi-Rad, and C. Faloutsos. Using ghost edges for classification in sparsely labeled networks. In Proceedings of the ACMSIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, New York, 2008, pp: 256.
  • E. W. Xiang. A Survey on Link Prediction Models for Social Network Data. Science And Technology (2008).
  • E. Zheleva, L. Getoor, J. Golbeck, and Ugur Kuter. Using friendship ties and family circles for link prediction. In Proceedings of the 2nd Workshop on Social Network Mining and Analysis, ACM Press, Ne
  • S. Redner. Teasing out the missing links. Nature 453 (2008) 47.
  • A. Clauset, C. Moore, and M.E.J. Newman. Hierarchical structure and the prediction of missing links in networks. Nature 453 (2008) 98.
  • 2007

  • M. Bilgic, G. M. Namata, and L. Getoor. Combining Collective Classification and Link Prediction. In Proceedings of the Seventh IEEE International Conference on Data Mining Workshops (ICDMW '07). IEEE Computer Society, Washington, DC, USA, 2007, pp: 381-386.
  • C. Wang, V. Satuluri, and S.Parthasarathy. Local Probabilistic Models for Link Prediction. In Proceedings of the 2007 Seventh IEEE International Conference on Data Mining (ICDM '07). IEEE Computer Society, Washington, DC, USA, 2007, pp:322-331.
  • K. Yu, W. Chu, S. Yu, V. Tresp, and Z. Xu. Stochastic relational models for discriminative link prediction. In Proceedings of Neural Information Precessing Systems, MIT Press, Cambridge, MA, 2007, pp. 1553–1560.
  • A. Potgieter, K. April, R. Cooke, and I. Osunmakinde. Temporality in Link Prediction: Understanding Social Complexity. Sprouts: Working Papers on Information Systems, 7 (2007).
  • Y. Liu, Z. Kou. Predicting who rated what in large scale datasets. SIGKDD Explor. Newsl. 9 (2007) 62.
  • D. Liben-Nowell and J. Kleinberg. The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58 (2007) 1019.
  • T. Murata and S. Moriyasu. Link prediction of social networks based on weighted proximity measure. In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, ACM Press, New York,
  • 2006

  • Z. Huang. Link Prediction Based on Graph Topology: The Predictive Value of the Generalized Clustering Coefficient. In Proceedings of the Workshop on Link Analysis: Dynamics and Static of Large Networks (2006).
  • H. Kashima, N. Abe. A Parameterized Probabilistic Model of Network Evolution for Supervised Link Prediction. In Proceedings of the Sixth International Conference on Data Mining, 2006, pp: 340-349.
  • M. A. Hasan, V. Chaoji S. Salem, and M. Zaki. Link prediction using supervised learning. In Proceeding of SDM 06 workshop on Link Analysis, Counterterrorism and Security, 2006.
  • Z. Huang and D.D. Zeng. A link prediction approach to anomalous email detection, in: Proceedings of 2006 IEEE International Conference on Systems, Man, and Cybernetics, Taipei, Taiwan, 2006, pp: 1131.
  • 2005

  • M. Qin, R. Zimmermann, and L. S. Liu. Supporting multimedia streaming between mobile peers with link availability prediction. In Proceedings of the 13th annual ACM international conference on Multimedia, 2005.
  • S. Martin, D. Roe, and J.L. Faulon. Predicting protein-protein interactions using signature products. Bioinformatics 21 (2005) 218.
  • M. J. Rattigan and D. Jensen. The case for anomalous link discovery. SIGKDD Explorations, 7 (2005).
  • J. O’Madadhain, P. Smyth, and L. Adamic. Learning predictive models for link formation. Presented at the International Sunbelt Social Network Conference, 2005.
  • J. O’Madadhain, J. Hutchins, and P. Smyth. Prediction and ranking algorithms for event-based network data. In Proceedings of SIGKDD 2005, ACM Press, New York, pp:23.
  • P. Holme and M. Huss. Role-similarity based functional prediction in networked systems: application to the yeast proteome, J. R. Soc. Interface 2 (2005) 327.
  • Z. Huang, X. Li, and H. Chen, Link prediction approach to collaborative filtering, in: Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries, ACM Press, New York, 2005.
  • L. Getoor and C.P. Diehl, Link mining: a survey, ACM SIGKDD Explor. Newsl. 7 (2005) 3.
  • 2004

  • B. Taskar, M.-F. Wong, P. Abbeel, and D. Koller, Link prediction in relational data. In Proceedings of Neural Information Precessing Systems, MIT Press, Cambridge, MA, 2004, pp: 659.
  • 2003

  • R. Jansen, et al.. A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data. Science, 302 (2003) 449.
  • A. Popescul and L. H. Ungar. Statistical relational learning for link prediction. In Proceedings of the Workshop on Learning Statistical Models from Relational Data. NewYork: ACM Press, 2003, pp:81-87
  • L. Qin and T. Kunz. Increasing Packet Delivery Ratio in DSR by Link Prediction. In Proceedings of the 36th Annual Hawaii International Conference on System Sciences, Washington, DC, USA, 9 (2003).
  • E. Weiss, K. Kurowski, S. Hischke, and B. Xu. Avoiding Route Breakage in Ad Hoc Networks using Link Prediction. iscc, pp.57, Eighth IEEE Symposium on Computers and Communications, 2003.
  • L. Getoor, N. Friedman, D. Koller, and B. Taskar. Learning probabilistic models of link structure. Journal of Machine Learning Research, 3:679–707, 2003.
  • 2002

  • J. Zhu, H. Hong, and J.G. Hughes. Using Markov models for web site link prediction. In Proceedings of the thirteenth ACM conference on Hypertext and hypermedia, 2002.
  • 2001

  • S. Jiang, D. He, and J. Rao. A prediction-based link availability estimation for mobile ad hoc networks. In Proccedings of the Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies, 2001.
  • 2000

  • R. R. Sarukkai. Link prediction and path analysis using markov chains. Computer Networks, 2000, 33(1-6): 377-386.
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