# Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding

arXiv: https://arxiv.org/abs/1905.10668 · HTML: https://mlsanity.com/paper/1905.10668/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2019.

## Built on by (most cited first, 29 of 29)

- [PinnerFormer: Sequence Modeling for User Representation at Pinterest](https://mlsanity.com/paper/2205.04507/) (2022)
- [Sparse-Interest Network for Sequential Recommendation](https://mlsanity.com/paper/2102.09267/) (2021)
- [On the Embedding Collapse when Scaling up Recommendation Models](https://mlsanity.com/paper/2310.04400/) (2023)
- [DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation](https://mlsanity.com/paper/2106.10879/) (2021)
- [Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection](https://mlsanity.com/paper/2212.12488/) (2022)
- [Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding](https://mlsanity.com/paper/2110.07582/) (2021)
- [DEGREE: Decomposition Based Explanation For Graph Neural Networks](https://mlsanity.com/paper/2305.12895/) (2023)
- [Dynamic Memory based Attention Network for Sequential Recommendation](https://mlsanity.com/paper/2102.09269/) (2021)
- [Graph Neural Networks: Methods, Applications, and Opportunities](https://mlsanity.com/paper/2108.10733/) (2021)
- [Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model](https://mlsanity.com/paper/2109.02058/) (2021)
- [Graph Contrastive Learning with Personalized Augmentation](https://mlsanity.com/paper/2209.06560/) (2022)
- [Learning Disentangled Representations for Time Series](https://mlsanity.com/paper/2105.08179/) (2021)
- [Collaborative Graph Neural Networks for Attributed Network Embedding](https://mlsanity.com/paper/2307.11981/) (2023)
- [Enhancing Idiomatic Representation in Multiple Languages via an Adaptive Contrastive Triplet Loss](https://mlsanity.com/paper/2406.15175/) (2024)
- [Large-Scale Network Embedding in Apache Spark](https://mlsanity.com/paper/2106.10620/) (2021)
- [Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models](https://mlsanity.com/paper/2305.01868/) (2023)
- [Graph Embedding with Hierarchical Attentive Membership](https://mlsanity.com/paper/2111.00604/) (2021)
- [MUSE: Multi-faceted Attention for Signed Network Embedding](https://mlsanity.com/paper/2104.14449/) (2021)
- [Deep Stable Multi-Interest Learning for Out-of-distribution Sequential Recommendation](https://mlsanity.com/paper/2304.05615/) (2023)
- [Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learning](https://mlsanity.com/paper/2407.20648/) (2024)
- [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)
- [Pointspectrum: Equivariance Meets Laplacian Filtering for Graph Representation Learning](https://mlsanity.com/paper/2109.02358/) (2021)
- [Cognitive and Cultural Topology of Linguistic Categories:A Semantic-Pragmatic Metric Approach](https://mlsanity.com/paper/2112.06876/) (2021)
- [Attention-based Dynamic Subspace Learners for Medical Image Analysis](https://mlsanity.com/paper/2206.09068/) (2022)
- [Improving Multi-Interest Network with Stable Learning](https://mlsanity.com/paper/2207.07910/) (2022)
- [PersonaSAGE: A Multi-Persona Graph Neural Network](https://mlsanity.com/paper/2212.13709/) (2022)
- [Aspect-Aware MOOC Recommendation in a Heterogeneous Network](https://mlsanity.com/paper/2602.05297/) (2026)
- [CONE: Embeddings for Complex Numerical Data Preserving Unit and Variable Semantics](https://mlsanity.com/paper/2603.04741/) (2026)

## Abstract (arXiv)

Networks have been widely used as the data structure for abstracting real-world systems as well as organizing the relations among entities. Network embedding models are powerful tools in mapping nodes in a network into continuous vector-space representations in order to facilitate subsequent tasks such as classification and link prediction. Existing network embedding models comprehensively integrate all information of each node, such as links and attributes, towards a single embedding vector to represent the node's general role in the network. However, a real-world entity could be multifaceted, where it connects to different neighborhoods due to different motives or self-characteristics that are not necessarily correlated. For example, in a movie recommender system, a user may love comedies or horror movies simultaneously, but it is not likely that these two types of movies are mutually close in the embedding space, nor the user embedding vector could be sufficiently close to them at the same time. In this paper, we propose a polysemous embedding approach for modeling multiple facets of nodes, as motivated by the phenomenon of word polysemy in language modeling. Each facet of a node is mapped as an embedding vector, while we also maintain association degree between each pair of node and facet. The proposed method is adaptive to various existing embedding models, without significantly complicating the optimization process. We also discuss how to engage embedding vectors of different facets for inference tasks including classification and link prediction. Experiments on real-world datasets help comprehensively evaluate the performance of the proposed method.