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Layer-diverse Negative Sampling for Graph Neural Networks
March 19, 2024, 4:41 a.m. | Wei Duan, Jie Lu, Yu Guang Wang, Junyu Xuan
cs.LG updates on arXiv.org arxiv.org
Abstract: Graph neural networks (GNNs) are a powerful solution for various structure learning applications due to their strong representation capabilities for graph data. However, traditional GNNs, relying on message-passing mechanisms that gather information exclusively from first-order neighbours (known as positive samples), can lead to issues such as over-smoothing and over-squashing. To mitigate these issues, we propose a layer-diverse negative sampling method for message-passing propagation. This method employs a sampling matrix within a determinantal point process, which …
abstract applications arxiv capabilities cs.lg data diverse gather gnns graph graph data graph neural networks however information layer negative networks neural networks positive representation samples sampling solution type
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