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Link Prediction Regression for Weighted Co-authorship Networks

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Advances in Computational Intelligence (IWANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11507))

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Abstract

In this paper, we study the problem of predicting quantity of collaborations in co-authorship network. We formulated our task in terms of link prediction problem on weighted co-authorship network, formed by authors writing papers in co-authorship represented by edges between authors in the network. Our task is formulated as regression for edge weights, for which we use node2vec network embedding and new family of edge embedding operators. We evaluate our model on AMiner co-authorship network and showed that our model of network edge representation has better performance for stated regression link prediction task.

Sections 1, 2 and 3 on “Knowledge representation, discovery, and processing: a logic-based approach” were supported by the Russian Science Foundation under grant 17-11-01294 and performed at National Research University Higher School of Economics, Russia. Sections 4 and 5 on “Knowledge acquisition and representation for recommender systems” were prepared within the framework of the HSE University Basic Research Program and funded by the Russian Academic Excellence Project ‘5-100’

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Author information

Authors and Affiliations

  1. National Research University Higher School of Economics, 3 Kochnovskiy Proezd, 125319, Moscow, Russia

    Ilya Makarov & Olga Gerasimova

  2. Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000, Ljubljana, Slovenia

    Ilya Makarov

Authors
  1. Ilya Makarov
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  2. Olga Gerasimova
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Correspondence to Ilya Makarov .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Barcelona, Spain

    Andreu Catala

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Makarov, I., Gerasimova, O. (2019). Link Prediction Regression for Weighted Co-authorship Networks. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11507. Springer, Cham. https://doi.org/10.1007/978-3-030-20518-8_55

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  • DOI: https://doi.org/10.1007/978-3-030-20518-8_55

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