# Attributed Network Embedding for Learning in a Dynamic Environment

arXiv: https://arxiv.org/abs/1706.01860 · HTML: https://mlsanity.com/paper/1706.01860/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2017.

## Built on by (most cited first, 30 of 39)

- [ROLAND: Graph Learning Framework for Dynamic Graphs](https://mlsanity.com/paper/2208.07239/) (2022)
- [A Survey on Graph Representation Learning Methods](https://mlsanity.com/paper/2204.01855/) (2022)
- [A Survey on Embedding Dynamic Graphs](https://mlsanity.com/paper/2101.01229/) (2021)
- [Dynamic Network Embedding Survey](https://mlsanity.com/paper/2103.15447/) (2021)
- [Dynamic Graph Collaborative Filtering](https://mlsanity.com/paper/2101.02844/) (2021)
- [A survey of dynamic graph neural networks](https://mlsanity.com/paper/2404.18211/) (2024)
- [Bi-CLKT: Bi-Graph Contrastive Learning based Knowledge Tracing](https://mlsanity.com/paper/2201.09020/) (2022)
- [DyTed: Disentangled Representation Learning for Discrete-time Dynamic Graph](https://mlsanity.com/paper/2210.10592/) (2022)
- [FeatureNorm: L2 Feature Normalization for Dynamic Graph Embedding](https://mlsanity.com/paper/2103.00164/) (2021)
- [Continual Graph Learning: A Survey](https://mlsanity.com/paper/2301.12230/) (2023)
- [Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Survey](https://mlsanity.com/paper/2203.10480/) (2022)
- [Pre-Training on Dynamic Graph Neural Networks](https://mlsanity.com/paper/2102.12380/) (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)
- [Deep Cut-informed Graph Embedding and Clustering](https://mlsanity.com/paper/2503.06635/) (2025)
- [DyGCN: Dynamic Graph Embedding with Graph Convolutional Network](https://mlsanity.com/paper/2104.02962/) (2021)
- [Instant Representation Learning for Recommendation over Large Dynamic Graphs](https://mlsanity.com/paper/2305.18622/) (2023)
- [Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs](https://mlsanity.com/paper/2210.00032/) (2022)
- [Efficient Dynamic Attributed Graph Generation](https://mlsanity.com/paper/2412.08810/) (2024)
- [Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction](https://mlsanity.com/paper/2110.03875/) (2021)
- [UniDyG: A Unified and Effective Representation Learning Approach for Large Dynamic Graphs](https://mlsanity.com/paper/2502.16431/) (2025)
- [Online Filtering over Expanding Graphs](https://mlsanity.com/paper/2301.06898/) (2023)
- [Graph-Based Representation Learning of Neuronal Dynamics and Behavior](https://mlsanity.com/paper/2410.00665/) (2024)
- [Network Embedding via Deep Prediction Model](https://mlsanity.com/paper/2104.13323/) (2021)
- [Learning User Embeddings from Temporal Social Media Data: A Survey](https://mlsanity.com/paper/2105.07996/) (2021)
- [Robust Dynamic Network Embedding via Ensembles](https://mlsanity.com/paper/2105.14557/) (2021)
- [ConvDySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention and Convolutional Neural Networks](https://mlsanity.com/paper/2106.11430/) (2021)
- [Semantic Graph Neural Network with Multi-measure Learning for Semi-supervised Classification](https://mlsanity.com/paper/2212.01749/) (2022)
- [Accelerating Dynamic Network Embedding with Billions of Parameter Updates to Milliseconds](https://mlsanity.com/paper/2306.08967/) (2023)
- [ForeSeer: Product Aspect Forecasting Using Temporal Graph Embedding](https://mlsanity.com/paper/2310.04865/) (2023)

## Abstract (arXiv)

Network embedding leverages the node proximity manifested to learn a low-dimensional node vector representation for each node in the network. The learned embeddings could advance various learning tasks such as node classification, network clustering, and link prediction. Most, if not all, of the existing works, are overwhelmingly performed in the context of plain and static networks. Nonetheless, in reality, network structure often evolves over time with addition/deletion of links and nodes. Also, a vast majority of real-world networks are associated with a rich set of node attributes, and their attribute values are also naturally changing, with the emerging of new content patterns and the fading of old content patterns. These changing characteristics motivate us to seek an effective embedding representation to capture network and attribute evolving patterns, which is of fundamental importance for learning in a dynamic environment. To our best knowledge, we are the first to tackle this problem with the following two challenges: (1) the inherently correlated network and node attributes could be noisy and incomplete, it necessitates a robust consensus representation to capture their individual properties and correlations; (2) the embedding learning needs to be performed in an online fashion to adapt to the changes accordingly. In this paper, we tackle this problem by proposing a novel dynamic attributed network embedding framework - DANE. In particular, DANE first provides an offline method for a consensus embedding and then leverages matrix perturbation theory to maintain the freshness of the end embedding results in an online manner. We perform extensive experiments on both synthetic and real attributed networks to corroborate the effectiveness and efficiency of the proposed framework.