# Node Embedding over Temporal Graphs

arXiv: https://arxiv.org/abs/1903.08889 · HTML: https://mlsanity.com/paper/1903.08889/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2019.

## Built on by (most cited first, 30 of 45)

- [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)
- [Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities](https://mlsanity.com/paper/2302.01018/) (2023)
- [A Survey on Embedding Dynamic Graphs](https://mlsanity.com/paper/2101.01229/) (2021)
- [Neighborhood-aware Scalable Temporal Network Representation Learning](https://mlsanity.com/paper/2209.01084/) (2022)
- [Continual Learning on Dynamic Graphs via Parameter Isolation](https://mlsanity.com/paper/2305.13825/) (2023)
- [SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs](https://mlsanity.com/paper/2305.13573/) (2023)
- [EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs](https://mlsanity.com/paper/2202.07648/) (2022)
- [FeatureNorm: L2 Feature Normalization for Dynamic Graph Embedding](https://mlsanity.com/paper/2103.00164/) (2021)
- [State Space Models on Temporal Graphs: A First-Principles Study](https://mlsanity.com/paper/2406.00943/) (2024)
- [Dynamic Graph Representation Learning with Neural Networks: A Survey](https://mlsanity.com/paper/2304.05729/) (2023)
- [Pre-Training on Dynamic Graph Neural Networks](https://mlsanity.com/paper/2102.12380/) (2021)
- [On the Equivalence Between Temporal and Static Graph Representations for Observational Predictions](https://mlsanity.com/paper/2103.07016/) (2021)
- [CCGL: Contrastive Cascade Graph Learning](https://mlsanity.com/paper/2107.12576/) (2021)
- [DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal Graphs](https://mlsanity.com/paper/2109.13441/) (2021)
- [Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation](https://mlsanity.com/paper/2304.07503/) (2023)
- [EqGNN: Equalized Node Opportunity in Graphs](https://mlsanity.com/paper/2108.08800/) (2021)
- [Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction](https://mlsanity.com/paper/2206.15005/) (2022)
- [Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs](https://mlsanity.com/paper/2305.10673/) (2023)
- [Geometric Algebra based Embeddings for Static and Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2202.09464/) (2022)
- [ConTIG: Continuous Representation Learning on Temporal Interaction Graphs](https://mlsanity.com/paper/2110.06088/) (2021)
- [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)
- [Event Detection on Dynamic Graphs](https://mlsanity.com/paper/2110.12148/) (2021)
- [Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand Prediction](https://mlsanity.com/paper/2205.14593/) (2022)
- [A Survey on Temporal Graph Representation Learning and Generative Modeling](https://mlsanity.com/paper/2208.12126/) (2022)
- [Temporal Link Prediction Using Graph Embedding Dynamics](https://mlsanity.com/paper/2401.07516/) (2024)
- [A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers](https://mlsanity.com/paper/2412.11293/) (2024)
- [Scalable and Efficient Temporal Graph Representation Learning via Forward Recent Sampling](https://mlsanity.com/paper/2402.01964/) (2024)
- [When Embedding Models Meet: Procrustes Bounds and Applications](https://mlsanity.com/paper/2510.13406/) (2025)

## Abstract (arXiv)

In this work, we present a method for node embedding in temporal graphs. We propose an algorithm that learns the evolution of a temporal graph's nodes and edges over time and incorporates this dynamics in a temporal node embedding framework for different graph prediction tasks. We present a joint loss function that creates a temporal embedding of a node by learning to combine its historical temporal embeddings, such that it optimizes per given task (e.g., link prediction). The algorithm is initialized using static node embeddings, which are then aligned over the representations of a node at different time points, and eventually adapted for the given task in a joint optimization. We evaluate the effectiveness of our approach over a variety of temporal graphs for the two fundamental tasks of temporal link prediction and multi-label node classification, comparing to competitive baselines and algorithmic alternatives. Our algorithm shows performance improvements across many of the datasets and baselines and is found particularly effective for graphs that are less cohesive, with a lower clustering coefficient.