Shallow Node Representation Learning using Centrality Indices

Paper in proceeding, 2021

In recent years, learning embeddings for nodes of a graph has become one of the most efficient w ays t o solve different graph problems such as link prediction, clustering and classification. I n t his p aper, w e p ropose a n ovel m ethod, called SECI, for learning embeddings of nodes, with application to link prediction. SECI samples from the network using breadth-first search and depth-first s earch, a nd i nterpolates b etween these two using centrality indices. The intuition behind SECI is that for nodes that have a low centrality score only a very small neighborhood is explored; and for dominant nodes that have a high centrality score a large neighborhood is explored. We evaluate the empirical performance of SECI over several realworld networks and show that it outperforms well-known existing algorithms.