Learning to Recommend Links using Graph Structure and Node Content
| Autor: | Freno, Antonino, Garriga, Gemma, Keller, Mikaela |
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| Přispěvatelé: | Keller, Mikaela, Modeling Tree Structures, Machine Learning, and Information Extraction (MOSTRARE), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria) |
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: | |
| Zdroj: | Neural Information Processing Systems Workshop on Choice Models and Preference Learning Neural Information Processing Systems Workshop on Choice Models and Preference Learning, Dec 2011, Granada, Spain |
| Popis: | International audience; The link prediction problem for graphs is a binary classification task that estimates the presence or absence of a link between two nodes in the graph. Links absent from the training set, however, cannot be directly considered as the negative examples since they might be present links at test time. Finding a hard decision boundary for link prediction is thus unnatural. This paper formalizes the link prediction problem from the flexible perspective of preference learning: the goal is to learn a preference score between any two nodes---either observed in the network at training time or to appear only later in the test---by using the feature vectors of the nodes and the structure of the graph as side information. Our assumption is that the observed edges, and in general, shortest paths between nodes in the graph, can reinforce an existing similarity between the nodes feature vectors. We propose a model implemented by a simple neural network architecture and an objective function that can be optimized by stochastic gradient descent over appropriate triplets of nodes in the graph. Our first preliminary experiments in small undirected graphs show that our learning algorithm outperforms baselines in real networks and is able to learn the correct distance function in synthetic networks. |
| Databáze: | OpenAIRE |
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