title: A neural network algorithm for semi-supervised node label learning from unbalanced data.
abstract: 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.
authors.affiliation: Dipartimento di Informatica, Università degli Studi di Milano, Via Comelico 39, 20135 Milano, Italy. frasca@di.unimi.it
authors.forename: Marco
authors.initials: M
authors.lastname: Frasca
authors.forename: Alberto
authors.initials: A
authors.lastname: Bertoni
authors.forename: Matteo
authors.initials: M
authors.lastname: Re
authors.forename: Giorgio
authors.initials: G
authors.lastname: Valentini
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007858:Learning / Q000502:physiology*; D016571:Neural Networks, Computer; D013223:Statistics as Topic