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.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

08 Faculty of Science > Institute of Computer Science (INF)

UniBE Contributor:

Fuchs, Mathias Christian, Riesen, Kaspar

Subjects:

000 Computer science, knowledge & systems
500 Science > 510 Mathematics

ISBN:

978-3-030-89657-7

Series:

Lecture Notes in Computer Science

Publisher:

Springer

Language:

English

Submitter:

Kaspar Riesen

Date Deposited:

21 Apr 2022 14:20

Last Modified:

05 Mar 2024 06:42

Publisher DOI:

10.1007/978-3-030-89657-7_26

BORIS DOI:

10.48350/166799

URI:

https://boris.unibe.ch/id/eprint/166799

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