June 7, 2024, 4:44 a.m. | Jiaxing Xu, Jinjie Ni, Yiping Ke

cs.LG updates on arXiv.org arxiv.org

arXiv:2209.00936v2 Announce Type: replace
Abstract: Graph Neural Networks (GNNs) are widely used for graph representation learning. Despite its prevalence, GNN suffers from two drawbacks in the graph classification task, the neglect of graph-level relationships, and the generalization issue. Each graph is treated separately in GNN message passing/graph pooling, and existing methods to address overfitting operate on each individual graph. This makes the graph representations learnt less effective in the downstream classification. In this paper, we propose a Class-Aware Representation rEfinement …

abstract arxiv class classification cs.ai cs.lg framework gnn gnns graph graph neural networks graph representation issue networks neural networks pooling relationships replace representation representation learning the graph type

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