Variational Recurrent Neural Networks for Graph Classification

7 Feb 2019 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... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Graph Classification ENZYMES VRGC Accuracy 48.4% # 25
Graph Classification MUTAG VRGC Accuracy 86.3% # 38
Graph Classification NCI1 VRGC Accuracy 80.7% # 20
Graph Classification PROTEINS VRGC Accuracy 74.8% # 37

Methods used in the Paper


METHOD TYPE
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