Variational Recurrent Neural Networks for Graph Classification
This work addresses graph classification for domains like molecular analysis, but it is incremental as it adapts existing NLP techniques to graphs.
The paper tackles graph classification using structural information by applying NLP-inspired sequential embedding and variational regularization, achieving state-of-the-art results on standard molecular datasets.
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.