Learning Probabilistic Models of Relational Structure
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
The Perceptron Algorithm with Uneven Margins
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning probabilistic models of link structure
The Journal of Machine Learning Research
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
On Constraint Sampling in the Linear Programming Approach to Approximate Dynamic Programming
Mathematics of Operations Research
Uncertain convex programs: randomized solutions and confidence levels
Mathematical Programming: Series A and B
Second Order Cone Programming Formulations for Feature Selection
The Journal of Machine Learning Research
The case for anomalous link discovery
ACM SIGKDD Explorations Newsletter
Clustering based large margin classification: a scalable approach using SOCP formulation
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A Parameterized Probabilistic Model of Network Evolution for Supervised Link Prediction
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Second Order Cone Programming Approaches for Handling Missing and Uncertain Data
The Journal of Machine Learning Research
Combining Collective Classification and Link Prediction
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Local Probabilistic Models for Link Prediction
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Using friendship ties and family circles for link prediction
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
Link prediction via matrix factorization
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
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In this paper, we consider the link prediction problem, where we are given a partial snapshot of a network at some time and the goal is to predict the additional links formed at a later time. The accuracy of current prediction methods is quite low due to the extreme class skew and the large number of potential links. Here, we describe learning algorithms based on chance constrained programs and show that they exhibit all the properties needed for a good link predictor, namely, they allow preferential bias to positive or negative class; handle skewness in the data; and scale to large networks. Our experimental results on three real-world domains--co-authorship networks, biological networks and citation networks--show significant performance improvement over baseline algorithms. We conclude by briefly describing some promising future directions based on this work.