稀疏标记图中用于节点分类的虚拟节点生成方法
Virtual Node Generation for Node Classification in Sparsely-Labeled Graphs
AI总结：
针对图领域稀疏标签下数据生成方法研究不足的问题，提出一种通过求解双重优化问题生成高质量虚拟节点注入图中以扩展标签传播的方法，兼容多种图学习范式，在10个公开数据集上较14个基线取得显著性能提升。
摘要：
在更广泛的机器学习研究领域中，数据生成方法通过扩充稀疏标签来生成额外的有信息训练样本，已展现出可观的效果。由于复杂拓扑结构中节点间存在复杂的依赖关系，这类方法在图领域的研究相对较少。本文提出一种新颖的节点生成方法，将少量高质量合成节点作为额外标记节点注入图中，以最优方式扩展标记信息的传播。仅通过注入额外节点，该框架与图学习及下游分类技术相互正交，因此可兼容大多数主流的图预训练（自监督学习）、半监督学习和元学习方法。本文的核心贡献在于通过求解一个全新的优化问题来设计生成节点集。该优化对生成节点的放置需满足两个条件：（1）最小化分类损失以保证训练精度；（2）最大化向下游任务中低置信度节点的标签传播以确保传播质量。理论上，我们证明上述双重优化可最大化节点分类的全局置信度。我们的实验显示，在10个公开数据集上，该方法相较于14个基线模型取得了具有统计学意义的性能提升。
英文摘要：
In the broader machine learning literature, data-generation methods demonstrate promising results by generating additional informative training examples via augmenting sparse labels. Such methods are less studied in graphs due to the intricate dependencies among nodes in complex topology structures. This paper presents a novel node generation method that infuses a small set of high-quality synthesized nodes into the graph as additional labeled nodes to optimally expand the propagation of labeled information. By simply infusing additional nodes, the framework is orthogonal to the graph learning and downstream classification techniques, and thus is compatible with most popular graph pre-training (self-supervised learning), semi-supervised learning, and meta-learning methods. The contribution lies in designing the generated node set by solving a novel optimization problem. The optimization places the generated nodes in a manner that: (1) minimizes the classification loss to guarantee training accuracy and (2) maximizes label propagation to low-confidence nodes in the downstream task to ensure high-quality propagation. Theoretically, we show that the above dual optimization maximizes the global confidence of node classification. Our Experiments demonstrate statistically significant performance improvements over 14 baselines on 10 publicly available datasets.
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。
