Normalized Cuts and Image Segmentation
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SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Automatic multimedia cross-modal correlation discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
The case for anomalous link discovery
ACM SIGKDD Explorations Newsletter
Fast Random Walk with Restart and Its Applications
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Spectral clustering and its use in bioinformatics
Journal of Computational and Applied Mathematics
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
IEEE Transactions on Knowledge and Data Engineering
Do you know?: recommending people to invite into your social network
Proceedings of the 14th international conference on Intelligent user interfaces
Graph nodes clustering with the sigmoid commute-time kernel: A comparative study
Data & Knowledge Engineering
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The slashdot zoo: mining a social network with negative edges
Proceedings of the 18th international conference on World wide web
A Recommender System Based on Local Random Walks and Spectral Methods
Advances in Web Mining and Web Usage Analysis
Learning spectral graph transformations for link prediction
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Fast approximate spectral clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Spectral clustering of biological sequence data
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Transitive node similarity for link prediction in social networks with positive and negative links
Proceedings of the fourth ACM conference on Recommender systems
Network growth and the spectral evolution model
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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Link prediction in protein-protein interaction networks (PPINs) is an important task in biology, since the vast majority of biological functions involve such protein interactions. Link prediction is also important for online social networks (OSNs), which provide predictions about who is a friend of whom. Many link prediction methods for PPINs/OSNs are local-based and do not exploit all network structure, which limits prediction accuracy. On the other hand, there are global approaches to detect the overall path structure in a network, being computationally prohibitive for huge-size PPINs/OSNs. In this paper, we enhance a previously proposed multi-way spectral clustering method by introducing new ways to capture node proximity in both PPINs/OSNs. Our new enhanced method uses information obtained from the top few eigenvectors of the normalized Laplacian matrix. As a result, it produces a less noisy matrix, which is smaller and more compact than the original one. In this way, we are able to provide faster and more accurate link predictions. Moreover, our new spectral clustering model is based on the well-known Bray-Curtis coefficient to measure proximity between two nodes. Compared to traditional clustering algorithms, such as k-means and DBSCAN, which assume globular (convex) regions in Euclidean space, our approach is more flexible in capturing the non-connected components of a social graph and a wider range of cluster geometries. We perform an extensive experimental comparison of the proposed method against existing link prediction algorithms and k-means algorithm, using two synthetic data sets, three real social networks and three real human protein data sets. Our experimental results show that our SpectralLink algorithm outperforms the local approaches, the k-means algorithm and another spectral clustering method in terms of effectiveness, whereas it is more efficient than the global approaches.