Related papers: Customized Graph Neural Networks

Customized Graph Neural Networks

URL: http://arxiv.org/abs/2005.12386v2
Date: Tue, 14 Dec 2021 06:24:21 GMT
Title: Customized Graph Neural Networks
Authors: Yiqi Wang, Yao Ma, Wei Jin, Chaozhuo Li, Charu Aggarwal, Jiliang Tang
Abstract summary: Graph Neural Networks (GNNs) have greatly advanced the task of graph classification. We propose a novel customized graph neural network framework, i.e., Customized-GNN. The proposed framework is very general that can be applied to numerous existing graph neural network models.
Score: 38.30640892828196
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Recently, Graph Neural Networks (GNNs) have greatly advanced the task of graph classification. Typically, we first build a unified GNN model with graphs in a given training set and then use this unified model to predict labels of all the unseen graphs in the test set. However, graphs in the same dataset often have dramatically distinct structures, which indicates that a unified model may be sub-optimal given an individual graph. Therefore, in this paper, we aim to develop customized graph neural networks for graph classification. Specifically, we propose a novel customized graph neural network framework, i.e., Customized-GNN. Given a graph sample, Customized-GNN can generate a sample-specific model for this graph based on its structure. Meanwhile, the proposed framework is very general that can be applied to numerous existing graph neural network models. Comprehensive experiments on various graph classification benchmarks demonstrate the effectiveness of the proposed framework.

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