Recent interest in graph embedding methods has focused on learning a single representation for each node in the graph. But can nodes really be best described by a single vector representation? In this work, we propose a method for learning multiple representations of the nodes in a graph (e.g., the users of a social network). Based on a principled decomposition of the ego-network, each representation encodes the role of the node in a different local community in which the nodes participate. These representations allow for improved reconstruction of the nuanced relationships that occur in the graph -- a phenomenon that we illustrate through state-of-the-art results on link prediction tasks on a variety of graphs, reducing the error by up to 90%. In addition, we show that these embeddings allow for effective visual analysis of the learned community structure.
@article{arxiv.1905.02138,
title = {Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts},
author = {Alessandro Epasto and Bryan Perozzi},
journal= {arXiv preprint arXiv:1905.02138},
year = {2019}
}