# Representation Learning over Dynamic Graphs

arXiv: https://arxiv.org/abs/1803.04051 · HTML: https://mlsanity.com/paper/1803.04051/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2018.

## Built on by (most cited first, 14 of 14)

- [ROLAND: Graph Learning Framework for Dynamic Graphs](https://mlsanity.com/paper/2208.07239/) (2022)
- [Pre-Training on Dynamic Graph Neural Networks](https://mlsanity.com/paper/2102.12380/) (2021)
- [Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes](https://mlsanity.com/paper/2401.04612/) (2024)
- [Enhancing the Expressivity of Temporal Graph Networks through Source-Target Identification](https://mlsanity.com/paper/2411.03596/) (2024)
- [Continuous Temporal Graph Networks for Event-Based Graph Data](https://mlsanity.com/paper/2205.15924/) (2022)
- [Permutation Equivariant Neural Controlled Differential Equations for Dynamic Graph Representation Learning](https://mlsanity.com/paper/2506.20324/) (2025)
- [Representing Social Networks as Dynamic Heterogeneous Graphs](https://mlsanity.com/paper/2209.03144/) (2022)
- [Learning Continually on a Sequence of Graphs -- The Dynamical System Way](https://mlsanity.com/paper/2305.12030/) (2023)
- [Preventing Conflicting Gradients in Neural Marked Temporal Point Processes](https://mlsanity.com/paper/2412.08590/) (2024)
- [DynBERG: Dynamic BERT-based Graph neural network for financial fraud detection](https://mlsanity.com/paper/2511.00047/) (2025)
- [DynaSTy: A Framework for SpatioTemporal Node Attribute Prediction in Dynamic Graphs](https://mlsanity.com/paper/2601.05391/) (2026)
- [Temporal Motif Signatures for Temporal Graph Neural Networks](https://mlsanity.com/paper/2606.01176/) (2026)
- [Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models](https://mlsanity.com/paper/2606.04672/) (2026)
- [Institutional Equity Holdings Prediction Using Node Affinities of Dynamic Graphs](https://mlsanity.com/paper/2607.12067/) (2026)

## Abstract (arXiv)

How can we effectively encode evolving information over dynamic graphs into low-dimensional representations? In this paper, we propose DyRep, an inductive deep representation learning framework that learns a set of functions to efficiently produce low-dimensional node embeddings that evolves over time. The learned embeddings drive the dynamics of two key processes namely, communication and association between nodes in dynamic graphs. These processes exhibit complex nonlinear dynamics that evolve at different time scales and subsequently contribute to the update of node embeddings. We employ a time-scale dependent multivariate point process model to capture these dynamics. We devise an efficient unsupervised learning procedure and demonstrate that our approach significantly outperforms representative baselines on two real-world datasets for the problem of dynamic link prediction and event time prediction.