Neural Graph Learning
| Autor: | Thang D. Bui, Vivek Ramavajjala, Sujith Ravi |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Artificial neural network
business.industry Computer science Document classification Computer Science::Neural and Evolutionary Computation Supervised learning 02 engineering and technology Semi-supervised learning computer.software_genre Graph ComputingMethodologies_PATTERNRECOGNITION Stochastic gradient descent Categorization 020204 information systems 0202 electrical engineering electronic engineering information engineering Labeled data 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | WSDM |
| Popis: | Label propagation is a powerful and flexible semi-supervised learning technique on graphs. Neural networks, on the other hand, have proven track records in many supervised learning tasks. In this work, we propose a training framework with a graph-regularised objective, namely Neural Graph Machines, that can combine the power of neural networks and label propagation. This work generalises previous literature on graph-augmented training of neural networks, enabling it to be applied to multiple neural architectures (Feed-forward NNs, CNNs and LSTM RNNs) and a wide range of graphs. The new objective allows the neural networks to harness both labeled and unlabeled data by: (a)~allowing the network to train using labeled data as in the supervised setting, (b)~biasing the network to learn similar hidden representations for neighboring nodes on a graph, in the same vein as label propagation. Such architectures with the proposed objective can be trained efficiently using stochastic gradient descent and scaled to large graphs, with a runtime that is linear in the number of edges. The proposed joint training approach convincingly outperforms many existing methods on a wide range of tasks (multi-label classification on social graphs, news categorization, document classification and semantic intent classification), with multiple forms of graph inputs (including graphs with and without node-level features) and using different types of neural networks. |
| Databáze: | OpenAIRE |
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