Recommending Co-authorship via Network Embeddings and Feature Engineering: The case of National Research University Higher School of Economics
Co-authorship networks contain hidden structural patterns of research collaboration. While some people may argue that the process of writing joint papers depends on mutual friendship, research interests, and university policy, we show that, given a temporal co-authorship network, one could predict the quality and quantity of future research publications. We are working on the comparison of existing graph embedding and feature engineering methods, presenting combined approach for constructing co-author recommender system formulated as link prediction problem. We also present a new link embedding operator improving the quality of link prediction base don embedding feature space. We evaluate our research on a single university publication dataset, providing meaningful interpretation of the obtained results.