论文标题
用于图形上半监督分类的基于扩散的算法一致的算法
A Consistent Diffusion-Based Algorithm for Semi-Supervised Classification on Graphs
论文作者
论文摘要
图表上的半监督分类旨在根据基于几个节点的标签（称为种子）分配标签。最流行的算法取决于热扩散的原理，其中种子的标签通过热传导散布，每个节点的温度用作每个标签的得分函数。使用简单的块模型，我们证明该算法是不一致的，除非节点的温度在分类前中心。我们表明，对算法的这种简单修改足以在实际数据上获得显着的性能提高。
Semi-supervised classification on graphs aims at assigning labels to all nodes of a graph based on the labels known for a few nodes, called the seeds. The most popular algorithm relies on the principle of heat diffusion, where the labels of the seeds are spread by thermo-conductance and the temperature of each node is used as a score function for each label. Using a simple block model, we prove that this algorithm is not consistent unless the temperatures of the nodes are centered before classification. We show that this simple modification of the algorithm is enough to get significant performance gains on real data.
