Abstract
In this paper, we consider new formulation of graph embedding algorithm, while learning node and edge representation under common constraints. We evaluate our approach on link prediction problem for co-authorship network of HSE researchers’ publications. We compare it with existing structural network embeddings and feature-engineering models.
The work was supported by the Russian Science Foundation under grant 17-11-01294 and performed at National Research University Higher School of Economics, Russia.
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Makarov, I., Gerasimova, O., Sulimov, P., Korovina, K., Zhukov, L.E. (2018). Joint Node-Edge Network Embedding for Link Prediction. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2018. Lecture Notes in Computer Science(), vol 11179. Springer, Cham. https://doi.org/10.1007/978-3-030-11027-7_3
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DOI: https://doi.org/10.1007/978-3-030-11027-7_3
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