Time Aware Index for Link Prediction in Social Networks

Link prediction in social networks such as collaboration networks and friendship networks have recently attracted a great deal of attention. There have been numerous attempts to address this problem through diverse approaches. In the present paper, we focus on the temporal behavior of the link strength, particularly the relationship between the time stamps of interactions or links and the temporal behavior of link strength and how link strength affects future link evolution. Most of the previous studies neglected the impact of time stamps of the interactions and of the links on link evolution. The gap between the current time and the time stamps of the interactions or links is also important to link evolution. In the present paper, we introduced a new time aware index, referred to as time score, that captures the important aspects of time stamps of interactions and the temporality of the link strengths. We apply time score to two social network data sets, namely, a coauthorship network data set and a Facebook friendship network data set. The results reveal a significant improvement in predicting future links.

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

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

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

[4]  Ryutaro Ichise,et al.  Semantic and Event-Based Approach for Link Prediction , 2008, PAKM.

[5]  Ryutaro Ichise,et al.  Finding Experts by Link Prediction in Co-authorship Networks , 2007, FEWS.

[6]  Francesco Bonchi,et al.  Cold start link prediction , 2010, KDD.

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

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

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

[10]  Lise Getoor,et al.  Link mining: a new data mining challenge , 2003, SKDD.

[11]  Srikanta J. Bedathur,et al.  Towards time-aware link prediction in evolving social networks , 2009, SNA-KDD '09.

[12]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[13]  Jon Kleinberg,et al.  The link prediction problem for social networks , 2003, CIKM '03.

[14]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[15]  Ryutaro Ichise,et al.  Using Abstract Information and Community Alignment Information for Link Prediction , 2010, 2010 Second International Conference on Machine Learning and Computing.

[16]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[17]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[18]  Krishna P. Gummadi,et al.  On the evolution of user interaction in Facebook , 2009, WOSN '09.

[19]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.