Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts
This addresses the limitation of single-embedding methods for graph nodes, particularly in social networks, by enabling nuanced relationship modeling, though it is incremental as it builds on existing graph embedding techniques.
The paper tackles the problem of representing nodes in graphs with a single vector, proposing a method to learn multiple representations per node based on ego-network decomposition, which captures different local community roles. This approach achieved state-of-the-art results on link prediction tasks, reducing error by up to 90%.
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.