June 10, 2024, 4:45 a.m. | Hsiao-Ying Lu, Yiran Li, Ujwal Pratap Krishna Kaluvakolanu Thyagarajan, Kwan-Liu Ma

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.04548v1 Announce Type: new
Abstract: Graph Neural Networks (GNNs) have proven highly effective in various machine learning (ML) tasks involving graphs, such as node/graph classification and link prediction. However, explaining the decisions made by GNNs poses challenges because of the aggregated relational information based on graph structure, leading to complex data transformations. Existing methods for explaining GNNs often face limitations in systematically exploring diverse substructures and evaluating results in the absence of ground truths. To address this gap, we introduce …

abstract arxiv challenges classification cs.ir cs.lg cs.si decisions evaluation gnns graph graph neural networks graphs however information link prediction machine machine learning networks neural networks node prediction relational tasks type

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