Graph Partition Neural Networks for Semi-Supervised Classification

Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander Gaunt, Raquel Urtasun, Richard S. Zemel (privately revealed to you)

Abstract:

We present graph partition neural networks (GPNN), an extension of graph neural
networks (GNNs) able to handle extremely large graphs.
GPNNs alternate between locally propagating information between nodes in small
subgraphs and globally propagating information between the subgraphs.
To efficiently partition graphs, we experiment with spectral partitioning and also
propose a modified multi-seed flood fill for fast processing of large scale graphs.
We extensively test our model on a variety of semi-supervised node
classification tasks.
Experimental results indicate that GPNNs are either superior or comparable to
state-of-the-art methods on a wide variety of datasets for graph-based
semi-supervised classification.
We also show that GPNNs can achieve similar performance as standard GNNs with
fewer propagation steps.

Code: [ Microsoft/graph-partition-neural-network-samples](https://github.com/Microsoft/graph-partition-neural-network-samples)

Data: [NELL](https://paperswithcode.com/dataset/nell), [Pubmed](https://paperswithcode.com/dataset/pubmed)

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/graph-partition-neural-networks-for-semi/code)