Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Signed networks in social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
Supervised Link Prediction Using Multiple Sources
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Low rank modeling of signed networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting Friends and Foes in Signed Networks Using Inductive Inference and Social Balance Theory
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
MATRI: a multi-aspect and transitive trust inference model
Proceedings of the 22nd international conference on World Wide Web
Different approaches to community evolution prediction in blogosphere
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Link label prediction in signed social networks
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Who proposed the relationship?: recovering the hidden directions of undirected social networks
Proceedings of the 23rd international conference on World wide web
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We consider the problem of link prediction in signed networks. Such networks arise on the web in a variety of ways when users can implicitly or explicitly tag their relationship with other users as positive or negative. The signed links thus created reflect social attitudes of the users towards each other in terms of friendship or trust. Our first contribution is to show how any quantitative measure of social imbalance in a network can be used to derive a link prediction algorithm. Our framework allows us to reinterpret some existing algorithms as well as derive new ones. Second, we extend the approach of Leskovec et al. (2010) by presenting a supervised machine learning based link prediction method that uses features derived from longer cycles in the network. The supervised method outperforms all previous approaches on 3 networks drawn from sources such as Epinions, Slashdot and Wikipedia. The supervised approach easily scales to these networks, the largest of which has 132k nodes and 841k edges. Most real-world networks have an overwhelmingly large proportion of positive edges and it is therefore easy to get a high overall accuracy at the cost of a high false positive rate. We see that our supervised method not only achieves good accuracy for sign prediction but is also especially effective in lowering the false positive rate.