Predicting Collaborations in Co-authorship Network
In this paper, we study the problem of predicting collaborations in co-authorship network. We formulated our task in terms of link prediction problem on weighted co-authorship network, in which authors play the role of nodes, and weighted edges connecting two authors are formed by storing either a number or quality metric of research papers co-authored by these authors. Our task is then formulated as regression machine learning model based on network features constructed using network embedding. We evaluate our edge embeddings on large AMiner co-authorship network for (un)weighted node2vec network embeddings and also on the dataset containing temporal information on National Research University Higher School of Economics (HSE) over twenty five years of research articles indexed in Russian Science Citation Index and Scopus for predicting the quality of future research publications measures in terms of quartiles corresponding to published journals indexed in Scopus. We showed that our model of network edge representation has better performance for stated regression task on both, AMiner and HSE co-authorship networks.