Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts

Alessandro Epasto; Bryan Perozzi

doi:10.1145/3308558.3313660

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arXiv · 1905.02138

Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts

Alessandro Epasto·Bryan Perozzi

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Abstract

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.

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Alessandro Epasto, Bryan Perozzi. 2019-05-06. Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts. https://doi.org/10.1145/3308558.3313660

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