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

## TL;DR

This paper introduces a method for learning multiple node embeddings in graphs, capturing different social contexts, which significantly improves link prediction accuracy and enables community visualization.

## Contribution

It presents a novel approach for generating multiple node representations based on ego-network decomposition, enhancing the modeling of complex social relationships.

## Key findings

- Up to 90% reduction in link prediction error
- Improved modeling of nuanced relationships
- Effective visualization of community structures

## 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.