# Attributed Social Network Embedding

arXiv: https://arxiv.org/abs/1705.04969 · HTML: https://mlsanity.com/paper/1705.04969/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2017.

## Built on by (most cited first, 30 of 34)

- [Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning](https://mlsanity.com/paper/2103.00113/) (2021)
- [Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddings](https://mlsanity.com/paper/2101.03091/) (2021)
- [Heterogeneous Temporal Graph Neural Network](https://mlsanity.com/paper/2110.13889/) (2021)
- [Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding](https://mlsanity.com/paper/2110.07582/) (2021)
- [A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications](https://mlsanity.com/paper/2404.14809/) (2024)
- [RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training](https://mlsanity.com/paper/2110.07336/) (2021)
- [Recommender systems based on graph embedding techniques: A comprehensive review](https://mlsanity.com/paper/2109.09587/) (2021)
- [Collaborative Graph Neural Networks for Attributed Network Embedding](https://mlsanity.com/paper/2307.11981/) (2023)
- [Generalizing Graph Transformers Across Diverse Graphs and Tasks via Pre-training](https://mlsanity.com/paper/2407.03953/) (2024)
- [GIPA: General Information Propagation Algorithm for Graph Learning](https://mlsanity.com/paper/2105.06035/) (2021)
- [Graph Based Link Prediction between Human Phenotypes and Genes](https://mlsanity.com/paper/2105.11989/) (2021)
- [Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees](https://mlsanity.com/paper/2209.01539/) (2022)
- [Robust Graph Representation Learning via Predictive Coding](https://mlsanity.com/paper/2212.04656/) (2022)
- [Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks](https://mlsanity.com/paper/2106.00717/) (2021)
- [Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective](https://mlsanity.com/paper/2201.08557/) (2022)
- [Digital Twin-Oriented Complex Networked Systems based on Heterogeneous Node Features and Interaction Rules](https://mlsanity.com/paper/2308.11034/) (2023)
- [Deep Dual Support Vector Data Description for Anomaly Detection on Attributed Networks](https://mlsanity.com/paper/2109.00138/) (2021)
- [Dynamic Bundling with Large Language Models for Zero-Shot Inference on Text-Attributed Graphs](https://mlsanity.com/paper/2505.17599/) (2025)
- [GUARD: Graph Universal Adversarial Defense](https://mlsanity.com/paper/2204.09803/) (2022)
- [Adaptive Local Clustering over Attributed Graphs](https://mlsanity.com/paper/2503.20488/) (2025)
- [A graph representation based on fluid diffusion model for data analysis: theoretical aspects and enhanced community detection](https://mlsanity.com/paper/2112.04388/) (2021)
- [Heterogeneous Feature Representation for Digital Twin-Oriented Complex Networked Systems](https://mlsanity.com/paper/2309.13229/) (2023)
- [A Scalable Inter-edge Correlation Modeling in CopulaGNN for Link Sign Prediction](https://mlsanity.com/paper/2601.19175/) (2026)
- [Deep Attributed Network Representation Learning via Attribute Enhanced Neighborhood](https://mlsanity.com/paper/2104.05234/) (2021)
- [On Representation Learning for Scientific News Articles Using Heterogeneous Knowledge Graphs](https://mlsanity.com/paper/2104.05866/) (2021)
- [CoANE: Modeling Context Co-occurrence for Attributed Network Embedding](https://mlsanity.com/paper/2106.09241/) (2021)
- [QUINT: Node embedding using network hashing](https://mlsanity.com/paper/2109.04206/) (2021)
- [Modeling Teams Performance Using Deep Representational Learning on Graphs](https://mlsanity.com/paper/2206.14741/) (2022)
- [Modeling the Social Influence of COVID-19 via Personalized Propagation with Deep Learning](https://mlsanity.com/paper/2207.13016/) (2022)
- [GIPA: A General Information Propagation Algorithm for Graph Learning](https://mlsanity.com/paper/2301.08209/) (2023)

## Abstract (arXiv)

Embedding network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification and entity retrieval. However, most existing methods focused only on leveraging network structure. For social networks, besides the network structure, there also exists rich information about social actors, such as user profiles of friendship networks and textual content of citation networks. These rich attribute information of social actors reveal the homophily effect, exerting huge impacts on the formation of social networks. In this paper, we explore the rich evidence source of attributes in social networks to improve network embedding. We propose a generic Social Network Embedding framework (SNE), which learns representations for social actors (i.e., nodes) by preserving both the structural proximity and attribute proximity. While the structural proximity captures the global network structure, the attribute proximity accounts for the homophily effect. To justify our proposal, we conduct extensive experiments on four real-world social networks. Compared to the state-of-the-art network embedding approaches, SNE can learn more informative representations, achieving substantial gains on the tasks of link prediction and node classification. Specifically, SNE significantly outperforms node2vec with an 8.2% relative improvement on the link prediction task, and a 12.7% gain on the node classification task.