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Found 3 papers, 2 papers with code
PiNet: Attention Pooling for Graph Classification
We propose PiNet, a generalised differentiable attention-based pooling mechanism for utilising graph convolution operations for graph level classification.
PiNet: A Permutation Invariant Graph Neural Network for Graph Classification
We propose an end-to-end deep learning learning model for graph classification and representation learning that is invariant to permutation of the nodes of the input graphs.
Ranked #60 on
Graph Classification
on PROTEINS
Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations
Building on prior work combining explicit tensor representations with a standard image-based classifier, we propose a model to perform graph classification by extracting fixed size tensorial information from each graph in a given set, and using a Capsule Network to perform classification.
Ranked #14 on
Graph Classification
on PTC
