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

arXiv: https://arxiv.org/abs/1905.02138 · HTML: https://mlsanity.com/paper/1905.02138/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2019.

## Built on by (most cited first, 19 of 19)

- [A Survey on Graph Representation Learning Methods](https://mlsanity.com/paper/2204.01855/) (2022)
- [Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning](https://mlsanity.com/paper/2202.13538/) (2022)
- [Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding](https://mlsanity.com/paper/2110.07582/) (2021)
- [Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model](https://mlsanity.com/paper/2109.02058/) (2021)
- [Large-Scale Network Embedding in Apache Spark](https://mlsanity.com/paper/2106.10620/) (2021)
- [Graph Embedding with Hierarchical Attentive Membership](https://mlsanity.com/paper/2111.00604/) (2021)
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- [Multiple Kernel Representation Learning on Networks](https://mlsanity.com/paper/2106.05057/) (2021)
- [A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods](https://mlsanity.com/paper/2112.10372/) (2021)
- [Graph Representation Learning Beyond Node and Homophily](https://mlsanity.com/paper/2203.01564/) (2022)
- [Everyone's Preference Changes Differently: Weighted Multi-Interest Retrieval Model](https://mlsanity.com/paper/2207.06652/) (2022)
- [Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learning](https://mlsanity.com/paper/2407.20648/) (2024)
- [Aitchison Embeddings for Learning Compositional Graph Representations](https://mlsanity.com/paper/2605.00716/) (2026)
- [mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network Embedding](https://mlsanity.com/paper/2104.02433/) (2021)
- [Multi-Aspect Temporal Network Embedding: A Mixture of Hawkes Process View](https://mlsanity.com/paper/2105.08566/) (2021)
- [Topic-aware latent models for representation learning on networks](https://mlsanity.com/paper/2111.05576/) (2021)
- [Cross-domain User Preference Learning for Cold-start Recommendation](https://mlsanity.com/paper/2112.03667/) (2021)
- [A Brief Survey on Representation Learning based Graph Dimensionality Reduction Techniques](https://mlsanity.com/paper/2211.05594/) (2022)
- [PersonaSAGE: A Multi-Persona Graph Neural Network](https://mlsanity.com/paper/2212.13709/) (2022)

## Abstract (arXiv)

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.