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LinkAUC: Unsupervised Evaluation of Multiple Network Node Ranks Using Link Prediction

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Complex Networks and Their Applications VIII (COMPLEX NETWORKS 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 881))

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Abstract

An emerging problem in network analysis is ranking network nodes based on their relevance to metadata groups that share attributes of interest, for example in the context of recommender systems or node discovery services. For this task, it is important to evaluate ranking algorithms and parameters and select the ones most suited to each network. Unfortunately, large real-world networks often comprise sparsely labelled nodes that hinder supervised evaluation, whereas unsupervised measures of community quality, such as density and conductance, favor structural characteristics that may not be indicative of metadata group quality. In this work, we introduce LinkAUC, a new unsupervised approach that evaluates network node ranks of multiple metadata groups by measuring how well they predict network edges. We explain that this accounts for relation knowledge encapsulated in known members of metadata groups and show that it enriches density-based evaluation. Experiments on one synthetic and two real-world networks indicate that LinkAUC agrees with AUC and NDCG for comparing ranking algorithms more than other unsupervised measures.

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Notes

  1. 1.

    https://snap.stanford.edu/data/.

  2. 2.

    Cosine similarity would arise by a fixed-flow assumption of the ranking algorithm that performs row-wise normalization of R before the dot product.

  3. 3.

    https://snap.stanford.edu/data/amazon-meta.html.

  4. 4.

    DBLP-Citation-network V4 from https://aminer.org/citation.

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Acknowledgements

This work was partially funded by the European Commission under contract numbers H2020-761634 FuturePulse and H2020-825585 HELIOS.

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Authors and Affiliations

  1. CERTH-ITI, Thessaloniki, Greece

    Emmanouil Krasanakis, Symeon Papadopoulos & Yiannis Kompatsiaris

Authors
  1. Emmanouil Krasanakis
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  2. Symeon Papadopoulos
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  3. Yiannis Kompatsiaris
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Corresponding author

Correspondence to Emmanouil Krasanakis .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  2. Università degli Studi di Milano, Milan, Italy

    Sabrina Gaito

  3. University of Aveiro, Aveiro, Portugal

    José Fernendo Mendes

  4. Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Indiana University, Bloomington, IN, USA

    Luis Mateus Rocha

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Krasanakis, E., Papadopoulos, S., Kompatsiaris, Y. (2020). LinkAUC: Unsupervised Evaluation of Multiple Network Node Ranks Using Link Prediction. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_1

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