The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
The case for anomalous link discovery
ACM SIGKDD Explorations Newsletter
Fast principal component analysis using fixed-point algorithm
Pattern Recognition Letters
Feature selection using principal feature analysis
Proceedings of the 15th international conference on Multimedia
Collaboration over time: characterizing and modeling network evolution
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Local Probabilistic Models for Link Prediction
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
The Unreasonable Effectiveness of Data
IEEE Intelligent Systems
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Towards time-aware link prediction in evolving social networks
Proceedings of the 3rd Workshop on Social Network Mining and Analysis
New perspectives and methods in link prediction
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
An analysis of the evolving coverage of computer science sub-fields in the DBLP digital library
ECDL'10 Proceedings of the 14th European conference on Research and advanced technology for digital libraries
Supervised Link Prediction Using Multiple Sources
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Supervised random walks: predicting and recommending links in social networks
Proceedings of the fourth ACM international conference on Web search and data mining
Co-author Relationship Prediction in Heterogeneous Bibliographic Networks
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
Managing the quality of person names in DBLP
ECDL'06 Proceedings of the 10th European conference on Research and Advanced Technology for Digital Libraries
Scalable Link Prediction on Multidimensional Networks
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
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Social networks are driven by social interaction and therefore dynamic. When modeled as a graph, nodes and links are continually added and deleted, and there is considerable interest in social network analysis on predicting link formation. Current work has not adequately addressed three issues: (1) Most link predictors start with using features from the link topology as input. How do features in other dimensions of the social network data affect link formation? (2) The dynamic nature of social networks implies the features driving link formation are constantly changing. How can a predictor automatically select the features that are important for link formation? (3) Node pairs that are not linked can outnumber links by orders of magnitude, but previous work do not address this imbalance. How can we design a predictor that is robust with respect to link imbalance? This paper presents sonLP, a social network link predictor. It uses principal component analysis to identify features that are important to link prediction, its tradeoff between true and false positives is near optimal for a wide range of link imbalance, and it has optimal time complexity. Experiments with coauthorship prediction in the ACM researcher community also show the importance of using features outside the links' dimension.