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Customizing Graph Neural Networks using Path Reweighting

arXiv 2106.10866 ↗First posted 21 Jun 2021cs.LGcited by 0

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Abstract (arXiv)

Graph Neural Networks (GNNs) have been extensively used for mining graph-structured data with impressive performance. However, because these traditional GNNs do not distinguish among various downstream tasks, embeddings embedded by them are not always effective. Intuitively, paths in a graph imply different semantics for different downstream tasks. Inspired by this, we design a novel GNN solution, namely Customized Graph Neural Network with Path Reweighting (CustomGNN for short). Specifically, the proposed CustomGNN can automatically learn the high-level semantics for specific downstream tasks to highlight semantically relevant paths as well to filter out task-irrelevant noises in a graph. Furthermore, we empirically analyze the semantics learned by CustomGNN and demonstrate its ability to avoid the three inherent problems in traditional GNNs, i.e., over-smoothing, poor robustness, and overfitting. In experiments with the node classification task, CustomGNN achieves state-of-the-art accuracies on three standard graph datasets and four large graph datasets. The source code of the proposed CustomGNN is available at urlhttps://github.com/cjpcool/CustomGNN.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 37 of its 65 references that are in the map. First posted on arXiv 21 Jun 2021.

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References · 37

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25 more
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