一种用于角色模型抽取的低秩相似性度量
Low-rank Similarity Measure for Role Model Extraction
摘要：
计算有意义的节点簇对于分析大型网络至关重要。在本文中，我们提出了一个成对的节点相似性度量，允许提取的角色，即一组节点在网络中共享相似的流模式。我们提出了一个低秩迭代方案来近似非常大的网络的相似性度量。最后，我们证明了我们的低秩相似性分数成功地提取了随机图中的不同角色，其性能与成对相似性度量相似。
英文摘要：
Computing meaningful clusters of nodes is crucial to analyze large networks. In this paper, we present a pairwise node similarity measure that allows to extract roles, i.e. group of nodes sharing similar flow patterns within a network. We propose a low rank iterative scheme to approximate the similarity measure for very large networks. Finally, we show that our low rank similarity score successfully extracts the different roles in random graphs and that its performances are similar to the pairwise similarity measure.
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。
