通过影响矛盾识别和纠正标签噪声以实现鲁棒图神经网络
Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction
AI总结：
提出ICGNN方法，利用图扩散矩阵计算影响矛盾分数（ICS）检测噪声标签，并通过邻居预测软策略纠正噪声标签，结合伪标签提升鲁棒性。
摘要：
图神经网络（GNN）在学习图结构数据方面表现出显著能力，广泛应用于社交分析和生物信息学等领域。然而，现实场景中标签噪声的存在对学习鲁棒GNN构成重大挑战，其有效性在处理图上噪声标签（通常源于标注错误或不一致）时会受到严重影响。为此，本文提出一种名为ICGNN的新方法，利用图的结构信息有效缓解噪声标签带来的挑战。具体地，我们首先设计一种新的噪声指示器，基于图扩散矩阵测量影响矛盾分数（ICS），以量化具有干净标签的节点的可信度，使得ICS值较高的节点更可能被检测为具有噪声标签。然后，我们利用高斯混合模型精确检测节点标签是否含有噪声。此外，我们开发了一种软策略，结合图上邻居节点的预测来纠正检测到的噪声标签。最后，引入大量未标记节点的伪标签，以提供辅助监督信号并指导模型优化。在基准数据集上的实验表明，我们的方法在噪声标签场景下优于竞争基线。
英文摘要：
Graph Neural Networks (GNNs) have shown remarkable capabilities in learning from graph-structured data with various applications such as social analysis and bioinformatics. However, the presence of label noise in real scenarios poses a significant challenge in learning robust GNNs, and their effectiveness can be severely impacted when dealing with noisy labels on graphs, often stemming from annotation errors or inconsistencies. To address this, in this paper we propose a novel approach called ICGNN that harnesses the structure information of the graph to effectively alleviate the challenges posed by noisy labels. Specifically, we first design a novel noise indicator that measures the influence contradiction score (ICS) based on the graph diffusion matrix to quantify the credibility of nodes with clean labels, such that nodes with higher ICS values are more likely to be detected as having noisy labels. Then we leverage the Gaussian mixture model to precisely detect whether the label of a node is noisy or not. Additionally, we develop a soft strategy to combine the predictions from neighboring nodes on the graph to correct the detected noisy labels. At last, pseudo-labeling for abundant unlabeled nodes is incorporated to provide auxiliary supervision signals and guide the model optimization. Experiments on benchmark datasets show the superiority of our approach over competitive baselines in noisy label scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
