Graph Embedding in Vector Spaces Using Matching-Graphs

Fuchs, Mathias; Riesen, Kaspar (2021). Graph Embedding in Vector Spaces Using Matching-Graphs. In: SISAP 2021 14th International Conference on Similarity Search and Applications, SISAP 2021. Lecture Notes in Computer Science: Vol. 13058 (pp. 352-363). Springer 10.1007/978-3-030-89657-7_26

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Graphs are recognized as a versatile alternative to feature vectors. That is, graphs are used in diverse applications (e.g. protein function/structure prediction, signature verification or detection of Alzheimer’s Disease). A large amount of graph based methods for pattern recognition have been proposed. Graph edit distance (GED) is one of the most flexible distance models available. We employ a suboptimal algorithm for computing the GED in polynomial time. This distance is denoted by dBP GED generally offers more information than merely a dissimilarity score, namely the information of the objects that actually match with each other (known as edit path). We exploit this information for graph embedding.

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