Link Prediction Measures in Various Types of Information Networks: A Review

An information network is represented as a graph where nodes represent entities and edges represent interactions between nodes. There can be multiple types of nodes and edges in such networks giving rise to homogeneous, multi-relational and heterogeneous networks. Link prediction problem is defined as predicting edges that are more likely to be formed in the network at a future time. Many measures have been proposed in the literature for homogeneous networks. Extensions of many of these measures to heterogeneous networks are not available. Further, the measures need to be redefined in order to utilize the weight and time information available with the interactions. In this work, along with the logical grouping of the measures as topological, probabilistic and linear algebraic measures for all types of networks, we fill the gaps by defining the measures where ever they are not available in the literature. The empirical evaluation of each of these measures in different types of networks on the DBLP benchmark dataset is presented. An overall improvement of 12% is observed in prediction accuracy when temporal and heterogeneous information is efficiently utilized.

[1]  Nitesh V. Chawla,et al.  Link Prediction: Fair and Effective Evaluation , 2012, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

[2]  Bin Chen,et al.  Predicting drug target interactions using meta-path-based semantic network analysis , 2016, BMC Bioinformatics.

[3]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[4]  David Liben-Nowell,et al.  The link-prediction problem for social networks , 2007 .

[5]  Barbara Carminati,et al.  User similarities on social networks , 2013, Social Network Analysis and Mining.

[6]  Lankeshwara Munasinghe,et al.  Time-aware methods for Link Prediction in Social Networks , 2013 .

[7]  T. Jaya Lakshmi,et al.  Link Prediction in Temporal Heterogeneous Networks , 2017, PAISI.

[8]  Fernando Berzal Galiano,et al.  A Survey of Link Prediction in Complex Networks , 2016, ACM Comput. Surv..

[9]  Jiawei Han,et al.  Mining advisor-advisee relationships from research publication networks , 2010, KDD.

[10]  Tsuyoshi Murata,et al.  Link Prediction based on Structural Properties of Online Social Networks , 2008, New Generation Computing.

[11]  M. Newman,et al.  Hierarchical structure and the prediction of missing links in networks , 2008, Nature.

[12]  Ricardo B. C. Prud.  Supervised Link Prediction in Weighted Networks , 2011 .

[13]  Philip S. Yu,et al.  PathSim , 2011, Proc. VLDB Endow..

[14]  Yoshihiro Yamanishi,et al.  propagation: A fast semisupervised learning algorithm for link prediction , 2009 .

[15]  A. Barabasi,et al.  Hierarchical Organization of Modularity in Metabolic Networks , 2002, Science.

[16]  Tamara G. Kolda,et al.  Temporal Link Prediction Using Matrix and Tensor Factorizations , 2010, TKDD.

[17]  Leo Katz,et al.  A new status index derived from sociometric analysis , 1953 .

[18]  Barbara Carminati,et al.  Network and profile based measures for user similarities on social networks , 2011, 2011 IEEE International Conference on Information Reuse & Integration.

[19]  Zhiguo Gong,et al.  Temporal PageRank on Social Networks , 2015, WISE.

[20]  Lada A. Adamic,et al.  Friends and neighbors on the Web , 2003, Soc. Networks.

[21]  Nitesh V. Chawla,et al.  Supervised methods for multi-relational link prediction , 2013, Social Network Analysis and Mining.

[22]  Alneu de Andrade Lopes,et al.  Link Prediction in Complex Networks Based on Cluster Information , 2012, SBIA.

[23]  Esra Gündoğan,et al.  A recommendation method based on link prediction in drug-disease bipartite network , 2017, 2017 2nd International Conference on Advanced Information and Communication Technologies (AICT).

[24]  T. Jaya Lakshmi,et al.  Temporal probabilistic measure for link prediction in collaborative networks , 2017, Applied Intelligence.

[25]  Shyhtsun Felix Wu,et al.  Analysis of user keyword similarity in online social networks , 2011, Social Network Analysis and Mining.

[26]  M. Newman.  Clustering and preferential attachment in growing networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[27]  T. Sørensen,et al.  A method of establishing group of equal amplitude in plant sociobiology based on similarity of species content and its application to analyses of the vegetation on Danish commons , 1948 .

[28]  Charu C. Aggarwal,et al.  Co-author Relationship Prediction in Heterogeneous Bibliographic Networks , 2011, 2011 International Conference on Advances in Social Networks Analysis and Mining.

[29]  Ying Ding,et al.  Applying weighted PageRank to author citation networks , 2011, J. Assoc. Inf. Sci. Technol..

[30]  Gita Reese Sukthankar,et al.  Link Prediction in Heterogeneous Collaboration Networks , 2014, Social Network Analysis.

[31]  Lise Getoor,et al.  Link mining: a survey , 2005, SKDD.

[32]  Ryutaro Ichise,et al.  Time Aware Index for Link Prediction in Social Networks , 2011, DaWaK.

[33]  Nicola Parolini,et al.  Link Prediction in Criminal Networks: A Tool for Criminal Intelligence Analysis , 2016, PloS one.

[34]  Ronald Rousseau,et al.  Similarity measures in scientometric research: The Jaccard index versus Salton's cosine formula , 1989, Inf. Process. Manag..

[35]  Peng Wang,et al.  Link prediction in social networks: the state-of-the-art , 2014, Science China Information Sciences.

[36]  Jérôme Kunegis,et al.  Learning spectral graph transformations for link prediction , 2009, ICML '09.

[37]  Dan Frankowski,et al.  Collaborative Filtering Recommender Systems , 2007, The Adaptive Web.

[38]  T. Jaya Lakshmi,et al.  Heterogeneous link prediction based on multi relational community information , 2014, 2014 Sixth International Conference on Communication Systems and Networks (COMSNETS).

[39]  Jure Leskovec,et al.  Effects of user similarity in social media , 2012, WSDM '12.

[40]  M. Newman,et al.  Vertex similarity in networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[41]  Nitesh V. Chawla,et al.  ALIVE: A Multi-relational Link Prediction Environment for the Healthcare Domain , 2012, PAKDD Workshops.

[42]  T. Jaya Lakshmi,et al.  Enhancement to community-based multi-relational link prediction using co-occurrence probability feature , 2015, CODS.

[43]  Mohammad Al Hasan,et al.  Link prediction using supervised learning , 2006 .

[44]  Srinivasan Parthasarathy,et al.  Local Probabilistic Models for Link Prediction , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[45]  Hsinchun Chen,et al.  Recommendation as link prediction in bipartite graphs: A graph kernel-based machine learning approach , 2013, Decis. Support Syst..

[46]  Sampo Pyysalo,et al.  Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches , 2018, BMC Bioinformatics.

[47]  Nitesh V. Chawla,et al.  New perspectives and methods in link prediction , 2010, KDD.