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GERoMe – A Novel Graph Extraction Robustness Measure

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Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 10310))

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Abstract

The extraction of graph structures in Euclidean vector space is a topic of interest with applications in many fields, e.g., the biomedical domain. While a number of different approaches have been presented, a quantitative evaluation of those algorithms remains a challenging task: Manual generation of ground truth for real-world data is often time-consuming and error-prone, and while tools for generating synthetic datasets with corresponding ground truth exist, this data often does not reflect the complexity in morphology and topology that real-world scenarios show. As a complementary or even alternative approach, we propose GERoMe, a novel graph extraction robustness measure, which quantifies the stability of algorithms that extract multigraphs with associated node positions from non-graph structures. Our method takes edge-associated properties into consideration and does not necessarily require ground truth data. Moreover, available ground truth information can be incorporated to additionally evaluate the correctness of the graph extraction algorithm. We demonstrate the usefulness and applicability of our approach in an exemplary study on synthetic and real-world data.

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Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany

    Dominik Drees, Aaron Scherzinger & Xiaoyi Jiang

Authors
  1. Dominik Drees

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  2. Aaron Scherzinger

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  3. Xiaoyi Jiang

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Corresponding author

Correspondence toDominik Drees.

Editor information

Editors and Affiliations

  1. Università degli Studi di Salerno, Fisciano, Italy

    Pasquale Foggia

  2. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  3. Università degli Studi di Salerno, Fisciano, Italy

    Mario Vento

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© 2017 Springer International Publishing AG

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Drees, D., Scherzinger, A., Jiang, X. (2017). GERoMe – A Novel Graph Extraction Robustness Measure. In: Foggia, P., Liu, CL., Vento, M. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2017. Lecture Notes in Computer Science(), vol 10310. Springer, Cham. https://doi.org/10.1007/978-3-319-58961-9_7

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