March  2015, 10(1): 17-35. doi: 10.3934/nhm.2015.10.17

Link prediction in multiplex networks

1. 

SPC, UP13, LIPN, CNRS UMR 7030, 99 Av. J.B. Clément, 93430 Villetaneuse, France, France

Received  July 2014 Revised  December 2014 Published  February 2015

In this work we present a new approach for co-authorship link prediction based on leveraging information contained in general bibliographical multiplex networks. A multiplex network is a graph defined over a set of nodes linked by different types of relations. For instance, the multiplex network we are studying here is defined as follows : nodes represent authors and links can be one of the following types: co-authorship links, co-venue attending links and co-citing links. A supervised-machine learning based link prediction approach is applied. A link formation model is learned based on a set of topological attributes describing both positive and negative examples. While such an approach has been successfully applied in the context on simple networks, different options can be applied to extend it to multiplex networks. One option is to compute topological attributes in each layer of the multiplex. Another one is to compute directly new multiplex-based attributes quantifying the multiplex nature of dyads (potential links). These different approaches are studied and compared through experiments on real datasets extracted from the bibliographical database DBLP.
Citation: Manisha Pujari, Rushed Kanawati. Link prediction in multiplex networks. Networks & Heterogeneous Media, 2015, 10 (1) : 17-35. doi: 10.3934/nhm.2015.10.17
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show all references

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[2]

First Monday, 8 (2003), 1995-2015. doi: 10.5210/fm.v8i6.1057.  Google Scholar

[3]

Statistical Analysis and Data Mining, 7 (2014), 14-33. doi: 10.1002/sam.11198.  Google Scholar

[4]

in Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '01, ACM, New York, NY, USA, 2001, 276-284. doi: 10.1145/383952.384007.  Google Scholar

[5]

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[6]

arXiv:1308.3182, (2013). Google Scholar

[7]

in Actes da la 17iéme Rencontre de la société francophone de classification (SFC'2010), St. Denis, La réunion, 2010, 63-66. Google Scholar

[8]

in Advances in Social Networks Analysis and Mining (ASONAM), 2011 International Conference on, IEEE, 2011, 485-489. doi: 10.1109/ASONAM.2011.103.  Google Scholar

[9]

World Wide Web, 16 (2013), 567-593. doi: 10.1007/s11280-012-0190-4.  Google Scholar

[10]

2nd edition, Kluwer Academic Publishing, 1998. Google Scholar

[11]

chapter Multi-Layered Social Networks, Springer, 2014. Google Scholar

[12]

Automation and Remote Control, 58 (1997), 1505-1514.  Google Scholar

[13]

in SOFSEM 2007: Theory and Practice of Computer Science, Lecture Notes in Computer Science, 4362, Springer-Verlag, Berlin-Heidelberg, 2007, 51-69. doi: 10.1007/978-3-540-69507-3_4.  Google Scholar

[14]

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[15]

J.-C. de Borda, Memoire sur les Elections au Scrutin,, 1781., ().   Google Scholar

[16]

in 2012 IEEE 12th International Conference on Data Mining (ICDM) (eds. M. J. Zaki, A. Siebes, J. X. Yu, B. Goethals, G. I. Webb and X. Wu), IEEE Computer Society, 2012, 181-190. doi: 10.1109/ICDM.2012.140.  Google Scholar

[17]

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[18]

in WWW '01: Proceedings of 10th International Conference on World Wide Web, 2001, 613-622. Google Scholar

[19]

in Sixth International Conference on Data Mining (ICDM'06), IEEE, 2006, 863-868. doi: 10.1109/ICDM.2006.18.  Google Scholar

[20]

in Proceedings of the 20th ACM International Conference on Information and Knowledge Management - CIKM '11, ACM Press, New York, New York, USA, 2011, 1169-1174. Available from: http://dblp.uni-trier.de/db/conf/cikm/cikm2011.html\#GaoDG11. doi: 10.1145/2063576.2063744.  Google Scholar

[21]

in Proc. of SDM 06 workshop on Link Analysis, Counterterrorism and Security, 2006. Google Scholar

[22]

in Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries (eds. M. Marlino, T. Sumner and F. M. S. III), ACM, 2005, 141-142. doi: 10.1145/1065385.1065415.  Google Scholar

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[25]

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[26]

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in Machine Learning and Knowledge Discovery in Databases (eds. D. Gunopulos, T. Hofmann, D. Malerba and M. Vazirgiannis), Lecture Notes in Computer Science, 6912, Springer Berlin Heidelberg, 2011, 437-452. doi: 10.1007/978-3-642-23783-6_28.  Google Scholar

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in WWW (Companion Volume) (eds. A. Mille, F. L. Gandon, J. Misselis, M. Rabinovich and S. Staab), ACM, 2012, 1189-1196. doi: 10.1145/2187980.2188260.  Google Scholar

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The European Physical Journal B, 71 (2009), 623-630. doi: 10.1140/epjb/e2009-00335-8.  Google Scholar

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