# dynnode2vec: Scalable Dynamic Network Embedding

arXiv: https://arxiv.org/abs/1812.02356 · HTML: https://mlsanity.com/paper/1812.02356/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2018.

## Built on by (most cited first, 30 of 32)

- [Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks](https://mlsanity.com/paper/2101.05974/) (2021)
- [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)
- [TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs](https://mlsanity.com/paper/2112.08025/) (2021)
- [Provably expressive temporal graph networks](https://mlsanity.com/paper/2209.15059/) (2022)
- [Neighborhood-aware Scalable Temporal Network Representation Learning](https://mlsanity.com/paper/2209.01084/) (2022)
- [Dynamic Network Embedding Survey](https://mlsanity.com/paper/2103.15447/) (2021)
- [TILP: Differentiable Learning of Temporal Logical Rules on Knowledge Graphs](https://mlsanity.com/paper/2402.12309/) (2024)
- [HGWaveNet: A Hyperbolic Graph Neural Network for Temporal Link Prediction](https://mlsanity.com/paper/2304.07302/) (2023)
- [DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal Graphs](https://mlsanity.com/paper/2109.13441/) (2021)
- [A Survey of Link Prediction in Temporal Networks](https://mlsanity.com/paper/2502.21185/) (2025)
- [Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks](https://mlsanity.com/paper/2306.06155/) (2023)
- [Recurrent Temporal Revision Graph Networks](https://mlsanity.com/paper/2309.12694/) (2023)
- [A Simple and Powerful Framework for Stable Dynamic Network Embedding](https://mlsanity.com/paper/2311.09251/) (2023)
- [Alignment and stability of embeddings: measurement and inference improvement](https://mlsanity.com/paper/2101.07251/) (2021)
- [Scalable and Efficient Temporal Graph Representation Learning via Forward Recent Sampling](https://mlsanity.com/paper/2402.01964/) (2024)
- [Robust Dynamic Network Embedding via Ensembles](https://mlsanity.com/paper/2105.14557/) (2021)
- [Network representation learning systematic review: ancestors and current development state](https://mlsanity.com/paper/2109.07583/) (2021)
- [DBGDGM: Dynamic Brain Graph Deep Generative Model](https://mlsanity.com/paper/2301.11408/) (2023)
- [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)
- [Local Intrinsic Dimensionality for Dynamic Graph Embeddings](https://mlsanity.com/paper/2411.16145/) (2024)
- [Trust in Motion: Capturing Trust Ascendancy in Open-Source Projects using Hybrid AI](https://mlsanity.com/paper/2210.02656/) (2022)
- [A Frequency-Structure Approach for Link Stream Analysis](https://mlsanity.com/paper/2212.03804/) (2022)
- [Space-Invariant Projection in Streaming Network Embedding](https://mlsanity.com/paper/2303.06293/) (2023)
- [ATEM: A Topic Evolution Model for the Detection of Emerging Topics in Scientific Archives](https://mlsanity.com/paper/2306.02221/) (2023)
- [An FPGA-Based Accelerator for Graph Embedding using Sequential Training Algorithm](https://mlsanity.com/paper/2312.15138/) (2023)
- [Invariant Graph Representations for Continuous-Time Dynamic Graphs Under Distribution Shifts](https://mlsanity.com/paper/2405.19062/) (2024)
- [RiskSEA : A Scalable Graph Embedding for Detecting On-chain Fraudulent Activities on the Ethereum Blockchain](https://mlsanity.com/paper/2410.02160/) (2024)
- [Representation learning of dynamic networks](https://mlsanity.com/paper/2412.11065/) (2024)

## Abstract (arXiv)

Network representation learning in low dimensional vector space has attracted considerable attention in both academic and industrial domains. Most real-world networks are dynamic with addition/deletion of nodes and edges. The existing graph embedding methods are designed for static networks and they cannot capture evolving patterns in a large dynamic network. In this paper, we propose a dynamic embedding method, dynnode2vec, based on the well-known graph embedding method node2vec. Node2vec is a random walk based embedding method for static networks. Applying static network embedding in dynamic settings has two crucial problems: 1) Generating random walks for every time step is time consuming 2) Embedding vector spaces in each timestamp are different. In order to tackle these challenges, dynnode2vec uses evolving random walks and initializes the current graph embedding with previous embedding vectors. We demonstrate the advantages of the proposed dynamic network embedding by conducting empirical evaluations on several large dynamic network datasets.