Variational Recurrent Neural Networks for Graph Classification

2019-02-07 · Edouard Pineau, Nathan de Lara

We address the problem of graph classification based only on structural information. Inspired by natural language processing techniques (NLP), our model sequentially embeds information to estimate class membership probabilities. Besides, we experiment with NLP-like variational regularization techniques, making the model predict the next node in the sequence as it reads it. We experimentally show that our model achieves state-of-the-art classification results on several standard molecular datasets. Finally, we perform a qualitative analysis and give some insights on whether the node prediction helps the model better classify graphs.

📄 PDF Abstract BibTeX arXiv:1902.02721

Code (1)

edouardpineau/Variational-Recurrent-Neural-Networks-for-Graph-Classification pytorch

Tasks

Classification General Classification Graph Classification

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