图的高阶标号均匀性与扩散
Higher-Order Label Homogeneity and Spreading in Graphs
摘要：
高阶网络结构有助于图半监督学习吗？给定一个图和一些标记的顶点，标记剩余的顶点是一个高影响力的问题，在几个任务中的应用，如推荐系统，欺诈检测和蛋白质识别。然而，传统的方法依赖于边缘来扩展标签，这是有限的，因为所有的边缘是不相等的。具有较强连接的顶点参与图中的高阶结构，这需要在半监督学习任务中利用这些结构的方法。为此，我们提出了高阶标签扩展（HOLS）来使用高阶结构扩展标签。HOLS有很强的理论保证，并减少到标准的标签传播的基本情况。通过大量的实验，我们表明，高阶标签传播使用三角形，除了边缘是高达4.7%优于标签传播单独使用的边缘。与现有的传统方法和最先进的方法相比，所提出的方法在除一种情况外的所有情况下都具有统计上显著的准确性增益，同时保持快速和可扩展到大型图形。
英文摘要：
Do higher-order network structures aid graph semi-supervised learning? Given a graph and a few labeled vertices, labeling the remaining vertices is a high-impact problem with applications in several tasks, such as recommender systems, fraud detection and protein identification. However, traditional methods rely on edges for spreading labels, which is limited as all edges are not equal. Vertices with stronger connections participate in higher-order structures in graphs, which calls for methods that can leverage these structures in the semi-supervised learning tasks. To this end, we propose Higher-Order Label Spreading (HOLS) to spread labels using higher-order structures. HOLS has strong theoretical guarantees and reduces to standard label spreading in the base case. Via extensive experiments, we show that higher-order label spreading using triangles in addition to edges is up to 4.7% better than label spreading using edges alone. Compared to prior traditional and state-of-the-art methods, the proposed method leads to statistically significant accuracy gains in all-but-one cases, while remaining fast and scalable to large graphs.
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。
