# TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation Learning

arXiv: https://arxiv.org/abs/2402.05396 · HTML: https://mlsanity.com/paper/2402.05396/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 43 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Builds on (most cited first, 30 of 34)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [GraphSAINT: Graph Sampling Based Inductive Learning Method](https://mlsanity.com/paper/1907.04931/) (2019)
- [FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling](https://mlsanity.com/paper/1801.10247/) (2018)
- [Temporal Graph Networks for Deep Learning on Dynamic Graphs](https://mlsanity.com/paper/2006.10637/) (2020)
- [Inductive Representation Learning on Temporal Graphs](https://mlsanity.com/paper/2002.07962/) (2020)
- [Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks](https://mlsanity.com/paper/1908.01207/) (2019)
- [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://mlsanity.com/paper/1902.10191/) (2019)
- [Adaptive Sampling Towards Fast Graph Representation Learning](https://mlsanity.com/paper/1809.05343/) (2018)
- [Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks](https://mlsanity.com/paper/1911.07323/) (2019)
- [Do We Really Need Complicated Model Architectures For Temporal Networks?](https://mlsanity.com/paper/2302.11636/) (2023)
- [Towards Better Evaluation for Dynamic Link Prediction](https://mlsanity.com/paper/2207.10128/) (2022)
- [TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs](https://mlsanity.com/paper/2203.14883/) (2022)
- [Dynamic Graph Neural Networks for Sequential Recommendation](https://mlsanity.com/paper/2104.07368/) (2021)
- [Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks](https://mlsanity.com/paper/2006.13866/) (2020)
- [Neighborhood-aware Scalable Temporal Network Representation Learning](https://mlsanity.com/paper/2209.01084/) (2022)
- [Bandit Samplers for Training Graph Neural Networks](https://mlsanity.com/paper/2006.05806/) (2020)
- [Accelerating Graph Sampling for Graph Machine Learning using GPUs](https://mlsanity.com/paper/2009.06693/) (2020)
- [Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs](https://mlsanity.com/paper/2106.06150/) (2021)
- [Safe Adaptive Importance Sampling](https://mlsanity.com/paper/1711.02637/) (2017)
- [A Biased Graph Neural Network Sampler with Near-Optimal Regret](https://mlsanity.com/paper/2103.01089/) (2021)
- [C-SAW: A Framework for Graph Sampling and Random Walk on GPUs](https://mlsanity.com/paper/2009.09103/) (2020)
- [DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training](https://mlsanity.com/paper/2307.07649/) (2023)
- [Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness](https://mlsanity.com/paper/2305.10863/) (2023)
- [Time-Aware Neighbor Sampling for Temporal Graph Networks](https://mlsanity.com/paper/2112.09845/) (2021)
- [Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares](https://mlsanity.com/paper/1803.00841/) (2018)

## Built on by (most cited first, 2 of 2)

- [Curriculum Negative Mining For Temporal Networks](https://mlsanity.com/paper/2407.17070/) (2024)
- [FLASH: Flexible Learning of Adaptive Sampling from History in Temporal Graph Neural Networks](https://mlsanity.com/paper/2504.07337/) (2025)

## Abstract (arXiv)

Recently, Temporal Graph Neural Networks (TGNNs) have demonstrated state-of-the-art performance in various high-impact applications, including fraud detection and content recommendation. Despite the success of TGNNs, they are prone to the prevalent noise found in real-world dynamic graphs like time-deprecated links and skewed interaction distribution. The noise causes two critical issues that significantly compromise the accuracy of TGNNs: (1) models are supervised by inferior interactions, and (2) noisy input induces high variance in the aggregated messages. However, current TGNN denoising techniques do not consider the diverse and dynamic noise pattern of each node. In addition, they also suffer from the excessive mini-batch generation overheads caused by traversing more neighbors. We believe the remedy for fast and accurate TGNNs lies in temporal adaptive sampling. In this work, we propose TASER, the first adaptive sampling method for TGNNs optimized for accuracy, efficiency, and scalability. TASER adapts its mini-batch selection based on training dynamics and temporal neighbor selection based on the contextual, structural, and temporal properties of past interactions. To alleviate the bottleneck in mini-batch generation, TASER implements a pure GPU-based temporal neighbor finder and a dedicated GPU feature cache. We evaluate the performance of TASER using two state-of-the-art backbone TGNNs. On five popular datasets, TASER outperforms the corresponding baselines by an average of 2.3% in Mean Reciprocal Rank (MRR) while achieving an average of 5.1x speedup in training time.