PiNet improves graph classification efficiency and accuracy.
problem Graph level classification challenges.
method Attention-based pooling mechanism for graph convolution operations.
result Superior performance and high sample efficiency.
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3 results for “chemo-informatics”
Statistical generative models for molecular graphs attract attention from many researchers from the fields of bio- and chemo-informatics. Among these models, invertible flow-based approaches are not fully explored yet. In this paper, we propose a powerful invertible flow for molecular graphs, called graph residual flow…
The recent advancements in graph neural networks (GNNs) have led to state-of-the-art performances in various applications, including chemo-informatics, question-answering systems, and recommender systems. However, scaling up these methods to huge graphs, such as social networks and Web graphs, remains a challenge. In p…