Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Computing the Inverse Matrix Hyperbolic Sine
NAA '00 Revised Papers from the Second International Conference on Numerical Analysis and Its Applications
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Distance-function design and fusion for sequence data
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Application of kernels to link analysis
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Introduction to Information Retrieval
Introduction to Information Retrieval
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Online-updating regularized kernel matrix factorization models for large-scale recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Classifying networked entities with modularity kernels
Proceedings of the 17th ACM conference on Information and knowledge management
Learning spectral graph transformations for link prediction
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Controversial users demand local trust metrics: an experimental study on Epinions.com community
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Link prediction for annotation graphs using graph summarization
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
Friendship prediction and homophily in social media
ACM Transactions on the Web (TWEB)
Online dating recommender systems: the split-complex number approach
Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web
Internal link prediction: A new approach for predicting links in bipartite graphs
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
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We define and study the link prediction problem in bipartite networks, specializing general link prediction algorithms to the bipartite case. In a graph, a link prediction function of two vertices denotes the similarity or proximity of the vertices. Common link prediction functions for general graphs are defined using paths of length two between two nodes. Since in a bipartite graph adjacency vertices can only be connected by paths of odd lengths, these functions do not apply to bipartite graphs. Instead, a certain class of graph kernels (spectral transformation kernels) can be generalized to bipartite graphs when the positive-semidefinite kernel constraint is relaxed. This generalization is realized by the odd component of the underlying spectral transformation. This construction leads to several new link prediction pseudokernels such as the matrix hyperbolic sine, which we examine for rating graphs, authorship graphs, folksonomies, document-feature networks and other types of bipartite networks.