Abstract
Classical graph approaches for pattern recognition applications rely on computing distances between graphs in the graph domain. That is, the distance between two graphs is obtained by directly optimizing some objective function which consider node and edge attributes. Bipartite Graph Matching was first published in a journal in 2009 and new versions have appeared to speed up its runtime such as the Fast Bipartite Graph Matching. This algorithm is based on defining a cost matrix between all nodes of both graphs and solving the node correspondence through a linear assignment method. To construct the matrix, several local structures can be defined from the simplest one (only the node) to the most complex (a whole clique or eigenvector structure). In this paper, we propose five different options and we show that the type of local structure and the distance defined between these structures is relevant for graph classification.
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Cortés, X., Serratosa, F., Moreno-García, C.F. (2015). On the Influence of Node Centralities on Graph Edit Distance for Graph Classification. In: Liu, CL., Luo, B., Kropatsch, W., Cheng, J. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2015. Lecture Notes in Computer Science(), vol 9069. Springer, Cham. https://doi.org/10.1007/978-3-319-18224-7_23
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DOI: https://doi.org/10.1007/978-3-319-18224-7_23
Publisher Name: Springer, Cham
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