论文标题
高阶标签同质性并在图中扩散
Higher-Order Label Homogeneity and Spreading in Graphs
论文作者
论文摘要
高阶网络结构是否有助于图形半监督学习？给定图形和一些标记的顶点，标记剩余的顶点是多个任务中应用程序的高度影响问题，例如推荐系统，欺诈检测和蛋白质识别。但是，传统方法依赖于传播标签的边缘，这是有限的，因为所有边缘都不相等。具有更强连接的顶点参与图中的高阶结构，该结构要求可以在半监督的学习任务中利用这些结构的方法。 为此，我们建议使用高阶结构扩展高阶标签扩展（HOLS）。 HOLS具有强大的理论保证，并减少了基本情况下的标准标签扩展。通过广泛的实验，我们表明，除边缘外，使用三角形的高阶标签扩展比单独使用边缘的标签扩展高4.7％。与先前的传统和最先进的方法相比，所提出的方法在所有但一种情况下都在统计上具有显着的准确性提高，同时保持快速且可扩展到大图。
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.
