论文标题
刺山柑：Coarsen，Align，Project，Refine-网络对齐的一般多级框架
CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment
论文作者
论文摘要
网络对齐方式或在不同网络中查找相应节点的任务是许多应用程序域中的重要问题。我们提出了CAPER，这是一个多级比对框架，它使输入图更粗糙，将粗糙的图对齐，将比对解决方案投射到更细的级别并完善对齐解决方案。我们表明，通过在多个图形分辨率上执行对齐一致性，CAPER可以改善许多不同的现有网络对齐算法：在较优质级别匹配的节点也应在更粗的级别上匹配。 CAPER还以线性时间变形和完善步骤的适度成本加速了使用较慢的网络对齐方法，通过允许它们在输入图的较小的较薄版本上运行。实验表明，刺山柑可以对不同的网络比对方法的改进，其准确性和/或运行时的准确性和/或数量级平均可以提高33％。
Network alignment, or the task of finding corresponding nodes in different networks, is an important problem formulation in many application domains. We propose CAPER, a multilevel alignment framework that Coarsens the input graphs, Aligns the coarsened graphs, Projects the alignment solution to finer levels and Refines the alignment solution. We show that CAPER can improve upon many different existing network alignment algorithms by enforcing alignment consistency across multiple graph resolutions: nodes matched at finer levels should also be matched at coarser levels. CAPER also accelerates the use of slower network alignment methods, at the modest cost of linear-time coarsening and refinement steps, by allowing them to be run on smaller coarsened versions of the input graphs. Experiments show that CAPER can improve upon diverse network alignment methods by an average of 33% in accuracy and/or an order of magnitude faster in runtime.
