论文标题
群集的图形匹配，用于标签恢复和图形分类
Clustered Graph Matching for Label Recovery and Graph Classification
论文作者
论文摘要
在给定一个顶点一致的网络和附加标签的网络的集合中，我们提出了一个程序，以利用顶点对准集合中的信号来恢复洗牌网络的标签。我们考虑在不同级别的粒度级别的顶点对准集合中将洗牌网络与网络的平均值匹配。我们在理论和实践中都证明，如果这些图来自不同的网络类，那么将网络聚类为类，然后将新图与群集大量匹配可以产生更高的保真度匹配性能，而不是匹配到全球平均图。此外，通过将图形匹配的目标函数相对于每个群集平均值最小化，此方法同时分类并恢复流浪图的顶点标签。通过匹配人类连接的真实数据实验，进一步加强了这些理论发展。
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.
