# Deep Representation Learning for Social Network Analysis

arXiv: https://arxiv.org/abs/1904.08547 · HTML: https://mlsanity.com/paper/1904.08547/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2019.

## Built on by (most cited first, 30 of 35)

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- [GiGaMAE: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstruction](https://mlsanity.com/paper/2308.09663/) (2023)
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- [Searching for High-Value Molecules Using Reinforcement Learning and Transformers](https://mlsanity.com/paper/2310.02902/) (2023)
- [Test-Time Training for Graph Neural Networks](https://mlsanity.com/paper/2210.08813/) (2022)
- [GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language Models](https://mlsanity.com/paper/2406.11945/) (2024)
- [Dynamic Memory based Attention Network for Sequential Recommendation](https://mlsanity.com/paper/2102.09269/) (2021)
- [Graph Contrastive Learning with Personalized Augmentation](https://mlsanity.com/paper/2209.06560/) (2022)
- [Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation](https://mlsanity.com/paper/2306.10534/) (2023)
- [Collaborative Graph Neural Networks for Attributed Network Embedding](https://mlsanity.com/paper/2307.11981/) (2023)
- [Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models](https://mlsanity.com/paper/2305.01868/) (2023)
- [Theoretical guarantees for the advantage of GNNs over NNs in generalizing bandlimited functions on Euclidean cubes](https://mlsanity.com/paper/2206.05904/) (2022)
- [GraphFM: A Comprehensive Benchmark for Graph Foundation Model](https://mlsanity.com/paper/2406.08310/) (2024)
- [Test-Time Training on Graphs with Large Language Models (LLMs)](https://mlsanity.com/paper/2404.13571/) (2024)
- [Deep Clustering with Self-Supervision using Pairwise Similarities](https://mlsanity.com/paper/2405.03590/) (2024)
- [DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research](https://mlsanity.com/paper/2309.01808/) (2023)
- [Marginal Nodes Matter: Towards Structure Fairness in Graphs](https://mlsanity.com/paper/2310.14527/) (2023)
- [SCALA: Sparsification-based Contrastive Learning for Anomaly Detection on Attributed Networks](https://mlsanity.com/paper/2401.01625/) (2024)
- [Data Augmentation in Graph Neural Networks: The Role of Generated Synthetic Graphs](https://mlsanity.com/paper/2407.14765/) (2024)
- [WGLE:Backdoor-free and Multi-bit Black-box Watermarking for Graph Neural Networks](https://mlsanity.com/paper/2506.08602/) (2025)
- [Multi-Agent Agentic Graph Learning via Structural Signatures](https://mlsanity.com/paper/2609.09565/) (2026)
- [Enabling Automated FPGA Accelerator Optimization Using Graph Neural Networks](https://mlsanity.com/paper/2111.08848/) (2021)
- [Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation](https://mlsanity.com/paper/2209.10168/) (2022)
- [Structural Imbalance Aware Graph Augmentation Learning](https://mlsanity.com/paper/2303.13757/) (2023)
- [Capturing Fine-grained Semantics in Contrastive Graph Representation Learning](https://mlsanity.com/paper/2304.11658/) (2023)
- [XInsight: Revealing Model Insights for GNNs with Flow-based Explanations](https://mlsanity.com/paper/2306.04791/) (2023)

## Abstract (arXiv)

Social network analysis is an important problem in data mining. A fundamental step for analyzing social networks is to encode network data into low-dimensional representations, i.e., network embeddings, so that the network topology structure and other attribute information can be effectively preserved. Network representation leaning facilitates further applications such as classification, link prediction, anomaly detection and clustering. In addition, techniques based on deep neural networks have attracted great interests over the past a few years. In this survey, we conduct a comprehensive review of current literature in network representation learning utilizing neural network models. First, we introduce the basic models for learning node representations in homogeneous networks. Meanwhile, we will also introduce some extensions of the base models in tackling more complex scenarios, such as analyzing attributed networks, heterogeneous networks and dynamic networks. Then, we introduce the techniques for embedding subgraphs. After that, we present the applications of network representation learning. At the end, we discuss some promising research directions for future work.