A new approach to the maximum-flow problem
Journal of the ACM (JACM)
Neural networks: a systematic introduction
Neural networks: a systematic introduction
Fast protein classification with multiple networks
Bioinformatics
Evolution of Networks: From Biological Nets to the Internet and WWW (Physics)
Evolution of Networks: From Biological Nets to the Internet and WWW (Physics)
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
The rendezvous algorithm: multiclass semi-supervised learning with Markov random walks
Proceedings of the 24th international conference on Machine learning
Nonlinear optimization using generalized hopfield networks
Neural Computation
Molecular Function Prediction Using Neighborhood Features
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Gene function prediction with gene interaction networks: a context graph kernel approach
IEEE Transactions on Information Technology in Biomedicine
True Path Rule Hierarchical Ensembles for Genome-Wide Gene Function Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
COSNet: a cost sensitive neural network for semi-supervised learning in graphs
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
A Fast Ranking Algorithm for Predicting Gene Functions in Biomolecular Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Discriminative sparse coding on multi-manifolds
Knowledge-Based Systems
Editorial: Partially supervised learning for pattern recognition
Pattern Recognition Letters
Pattern classification and clustering: A review of partially supervised learning approaches
Pattern Recognition Letters
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Given a weighted graph and a partial node labeling, the graph classification problem consists in predicting the labels of all the nodes. In several application domains, from gene to social network analysis, the labeling is unbalanced: for instance positive labels may be much less than negatives. In this paper we present COSNet (COst Sensitive neural Network), a neural algorithm for predicting node labels in graphs with unbalanced labels. COSNet is based on a 2-parameter family of Hopfield networks, and consists of two main steps: (1) the network parameters are learned through a cost-sensitive optimization procedure; (2) a suitable Hopfield network restricted to the unlabeled nodes is considered and simulated. The reached equilibrium point induces the classification of the unlabeled nodes. The restriction of the dynamics leads to a significant reduction in time complexity and allows the algorithm to nicely scale with large networks. An experimental analysis on real-world unbalanced data, in the context of the genome-wide prediction of gene functions, shows the effectiveness of the proposed approach.