标题： 聚类图匹配用于标签恢复和图分类 显示英文标题

标题： Clustered Graph Matching for Label Recovery and Graph Classification

摘要： 给定一组顶点对齐的网络和一个额外的标签乱序网络，我们提出了利用顶点对齐集合中的信号来恢复乱序网络标签的方法。 我们考虑将乱序网络与顶点对齐集合中不同粒度的网络平均值进行匹配。 我们从理论上和实践中都证明了，如果这些图来自不同的网络类别，则首先将网络聚类到类别中，然后将新图与类别均值进行匹配，可以比与全局均值图匹配获得更高的保真度匹配性能。 此外，通过最小化每个类别均值的图匹配目标函数，这种方法同时实现了分类并恢复乱序图的顶点标签。 通过一个引人注目的真实数据实验——匹配人类连接组，进一步加强了这些理论发展。 显示英文摘要

摘要： Given a collection of vertex-aligned networks and an additional label-shuffled network, we propose procedures for leveraging the signal in the vertex-aligned collection to recover the labels of the shuffled network. We consider matching the shuffled network to averages of the networks in the vertex-aligned collection at different levels of granularity. We demonstrate both in theory and practice that if the graphs come from different network classes, then clustering the networks into classes followed by matching the new graph to cluster-averages can yield higher fidelity matching performance than matching to the global average graph. Moreover, by minimizing the graph matching objective function with respect to each cluster average, this approach simultaneously classifies and recovers the vertex labels for the shuffled graph. These theoretical developments are further reinforced via an illuminating real data experiment matching human connectomes.