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Gotta match 'em all: Solution diversification in graph matching matched filters
We present a novel approach for finding multiple noisily embedded template graphs in a very large background graph.
Clustered Graph Matching for Label Recovery and Graph Classification
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.
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