Summary of Taser: Temporal Adaptive Sampling For Fast and Accurate Dynamic Graph Representation Learning, by Gangda Deng et al.
TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation Learning
by Gangda Deng, Hongkuan Zhou, Hanqing Zeng, Yinglong Xia, Christopher Leung, Jianbo Li, Rajgopal Kannan, Viktor Prasanna
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to improving the performance and efficiency of Temporal Graph Neural Networks (TGNNs). TGNNs have achieved state-of-the-art results in applications like fraud detection and content recommendation, but they are susceptible to noise in real-world dynamic graphs. The authors identify two critical issues: models being supervised by inferior interactions and noisy input inducing high variance. To address these challenges, they introduce TASER, an adaptive sampling method optimized for accuracy, efficiency, and scalability. TASER adapts its mini-batch selection based on training dynamics and temporal neighbor selection based on contextual, structural, and temporal properties of past interactions. The authors evaluate TASER using two state-of-the-art backbone TGNNs on five popular datasets, achieving a 2.3% average improvement in Mean Reciprocal Rank (MRR) and a 5.1x average speedup in training time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to understand a big social network with lots of people talking to each other over time. Right now, the computers we use to analyze this data can make mistakes because some of the connections between people are fake or not very important. To fix this problem, researchers have developed special computer models called Temporal Graph Neural Networks (TGNNs). These models are really good at understanding social networks, but they can still get confused by all the noise in the data. In this paper, the authors suggest a new way to make these models work better and faster. They call it TASER, and it helps the models focus on the most important connections between people. By using TASER, the authors were able to make their models more accurate and efficient, which is really important for real-world applications like detecting fake accounts or recommending content. |
Keywords
* Artificial intelligence * Supervised
