The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Growth of the flickr social network
Proceedings of the first workshop on Online social networks
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Local Probabilistic Models for Link Prediction
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Scalable proximity estimation and link prediction in online social networks
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
New perspectives and methods in link prediction
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Temporal Link Prediction Using Matrix and Tensor Factorizations
ACM Transactions on Knowledge Discovery from Data (TKDD)
Supervised random walks: predicting and recommending links in social networks
Proceedings of the fourth ACM international conference on Web search and data mining
Exploiting place features in link prediction on location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
LinkBoost: A Novel Cost-Sensitive Boosting Framework for Community-Level Network Link Prediction
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Clustered embedding of massive social networks
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
Organizational overlap on social networks and its applications
Proceedings of the 22nd international conference on World Wide Web
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The automated analysis of social networks has become an important problem due to the proliferation of social networks, such as LiveJournal, Flickr and Facebook. The scale of these social networks is massive and continues to grow rapidly. An important problem in social network analysis is proximity estimation that infers the closeness of different users. Link prediction, in turn, is an important application of proximity estimation. However, many methods for computing proximity measures have high computational complexity and are thus prohibitive for large-scale link prediction problems. One way to address this problem is to estimate proximity measures via low-rank approximation. However, a single low-rank approximation may not be sufficient to represent the behavior of the entire network. In this paper, we propose Multi-Scale Link Prediction (MSLP), a framework for link prediction, which can handle massive networks. The basic idea of MSLP is to construct low-rank approximations of the network at multiple scales in an efficient manner. To achieve this, we propose a fast tree-structured approximation algorithm. Based on this approach, MSLP combines predictions at multiple scales to make robust and accurate predictions. Experimental results on real-life datasets with more than a million nodes show the superior performance and scalability of our method.