March 13, 2024, 4:43 a.m. | Jianpeng Chen, Yujing Wang, Ming Zeng, Zongyi Xiang, Bitan Hou, Yunhai Tong, Ole J. Mengshoel, Yazhou Ren

cs.LG updates on arXiv.org arxiv.org

arXiv:2106.10866v3 Announce Type: replace
Abstract: 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 …

arxiv cs.lg graph graph neural networks networks neural networks path type

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