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GSM: Inductive Learning on Dynamic Graph Embeddings

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Network Algorithms, Data Mining, and Applications (NET 2018)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 315))

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Abstract

In this paper, we study the problem of learning graph embeddings for dynamic networks and the ability to generalize to unseen nodes called inductive learning. Firstly, we overview the state-of-the-art methods and techniques for constructing graph embeddings and learning algorithms for both transductive and inductive approaches. Secondly, we propose an improved GSM based on GraphSAGE algorithm and set up the experiments on datasets CORA, Reddit, and HSEcite, which is collected from Scopus citation database across the authors with affiliation to NRU HSE in 2011–2017. The results show that our three-layer model with attention-based aggregation function, added normalization layers, regularization (dropout) outperforms suggested by the respective authors’ GraphSAGE models with mean, LSTM, and pool aggregation functions, thus giving more insight into possible ways to improve inducting learning model based on GraphSAGE model.

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Acknowledgements

Sections 2–5 were prepared under the support by the Russian Science Foundation under grant 17-11-01294, performed at National Research University Higher School of Economics, Russia. Section 1 was prepared under support by RFBR grant 16-29-09583 “Development of methodology, methods and tools for identifying and countering the proliferation of malicious information campaigns in the Internet”.

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Authors and Affiliations

  1. National Research University Higher School of Economics, 3 Kochnovskiy Proezd, 107207, Moscow, Russian Federation

    Marina Ananyeva & Ilya Makarov

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

    Ilya Makarov

  3. Analytical Software Solutions LLC, 3 proezd Marinoy Roshchi 40, bld. 1, 127018, Moscow, Russian Federation

    Mikhail Pendiukhov

Authors
  1. Marina Ananyeva
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  2. Ilya Makarov
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  3. Mikhail Pendiukhov
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Corresponding author

Correspondence to Ilya Makarov .

Editor information

Editors and Affiliations

  1. Higher School of Economics, National Research University, Nizhny Novgorod, Russia

    Ilya Bychkov

  2. Higher School of Economics, National Research University, Nizhny Novgorod, Russia

    Valery A. Kalyagin

  3. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  4. Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, PA, USA

    Oleg Prokopyev

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Ananyeva, M., Makarov, I., Pendiukhov, M. (2020). GSM: Inductive Learning on Dynamic Graph Embeddings. In: Bychkov, I., Kalyagin, V., Pardalos, P., Prokopyev, O. (eds) Network Algorithms, Data Mining, and Applications. NET 2018. Springer Proceedings in Mathematics & Statistics, vol 315. Springer, Cham. https://doi.org/10.1007/978-3-030-37157-9_6

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