A Novel Global Prototype-Based Node Embedding Technique

Abstract

Node embedding refers to learning or generating low-dimensional representations for nodes in a given graph. In the era of big data and large graphs, there has been a growing interest in node embedding across a wide range of applications, ranging from social media to healthcare. Numerous research efforts have been invested in searching for node embeddings that maximally preserve the associated graph properties. However, each embedding technique has its own relative disadvantages. This paper presents a method for generating deep neural node embeddings that encode dissimilarity scores between pairs of nodes with the help of prototype nodes spread throughout the target graph. The technique we develop is adaptable to various notions of dissimilarity and yields efficient embeddings capable of estimating the dissimilarity between any two pairs of nodes in a graph. We compare our technique against some state-of-the-art similar embedding techniques and demonstrate superior results in a number of experiments using several benchmark datasets.