Graph Partial Label Learning with Potential Cause Discovering

• URL: http://arxiv.org/abs/2403.11449v3
• Date: Thu, 22 Aug 2024 01:57:40 GMT
• Title: Graph Partial Label Learning with Potential Cause Discovering
• Authors: Hang Gao, Jiaguo Yuan, Jiangmeng Li, Peng Qiao, Fengge Wu, Changwen Zheng, Huaping Liu,
• Abstract summary: Graph Networks (GNNs) have garnered widespread attention for their potential to address the challenges posed by graph representation learning. Due to the inherent complexity and interconnectedness of graphs, accurately annotating graph data for training GNNs is extremely challenging.
• Score: 24.659793052786814
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph Neural Networks (GNNs) have garnered widespread attention for their potential to address the challenges posed by graph representation learning, which face complex graph-structured data across various domains. However, due to the inherent complexity and interconnectedness of graphs, accurately annotating graph data for training GNNs is extremely challenging. To address this issue, we have introduced Partial Label Learning (PLL) into graph representation learning. PLL is a critical weakly supervised learning problem where each training instance is associated with a set of candidate labels, including the ground-truth label and the additional interfering labels. PLL allows annotators to make errors, which reduces the difficulty of data labeling. Subsequently, we propose a novel graph representation learning method that enables GNN models to effectively learn discriminative information within the context of PLL. Our approach utilizes potential cause extraction to obtain graph data that holds causal relationships with the labels. By conducting auxiliary training based on the extracted graph data, our model can effectively eliminate the interfering information in the PLL scenario. We support the rationale behind our method with a series of theoretical analyses. Moreover, we conduct extensive evaluations and ablation studies on multiple datasets, demonstrating the superiority of our proposed method.

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