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Vertex nomination via seeded graph matching
Statistical Analysis and Data Mining ( IF 1.3 ) Pub Date : 2020-03-16 , DOI: 10.1002/sam.11454
Heather G. Patsolic 1 , Youngser Park 2 , Vince Lyzinski 3 , Carey E. Priebe 1, 2
Affiliation  

Consider two networks on overlapping, nonidentical vertex sets. Given vertices of interest (VOIs) in the first network, we seek to identify the corresponding vertices, if any exist, in the second network. While in moderately sized networks graph matching methods can be applied directly to recover the missing correspondences, herein we present a principled methodology appropriate for situations in which the networks are too large/noisy for brute‐force graph matching. Our methodology identifies vertices in a local neighborhood of the VOIs in the first network that have verifiable corresponding vertices in the second network. Leveraging these known correspondences, referred to as seeds, we match the induced subgraphs in each network generated by the neighborhoods of these verified seeds, and rank the vertices of the second network in terms of the most likely matches to the original VOIs. We demonstrate the applicability of our methodology through simulations and real data examples.

中文翻译:

通过种子图匹配指定顶点

考虑重叠的,不同的顶点集上的两个网络。给定第一个网络中的关注顶点(VOI),我们试图确定第二个网络中相应的顶点(如果存在)。尽管在中等规模的网络中,可以将图匹配方法直接用于恢复丢失的对应关系,但在此,我们提出一种适用于其中网络太大/对于强力图匹配而言嘈杂的情况的原理方法。我们的方法确定了第一网络中VOI的本地邻域中的顶点,这些顶点在第二网络中具有可验证的对应顶点。利用这些已知的对应关系(称为种子),我们匹配由这些经过验证的种子的邻域生成的每个网络中的诱导子图,并根据最有可能与原始VOI匹配的方式对第二个网络的顶点进行排名。我们通过仿真和实际数据示例来证明我们的方法的适用性。
更新日期:2020-03-16
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