Edge-centric network analysis

Abstract

Most of the existing deep-learning-based network analysis techniques focus on the problem of learning low-dimensional node representations. However, networks can also be seen in the light of edges interlinking pairs of nodes. The broad goal of this paper is to introduce a deep-learning framework focused on computing edge-centric network embeddings. We present a novel approach called ECNE, which instead of computing edge embeddings by aggregating node embeddings, computes them directly. ECNE leverages the notion of line graph of a graph coupled with an edge weighting mechanism to preserve the dynamic of the original graph in the line graph. We show that ECNE brings benefits wrt the state-of-the-art.