Summary of Dyg-mamba: Continuous State Space Modeling on Dynamic Graphs, by Dongyuan Li and Shiyin Tan and Ying Zhang and Ming Jin and Shirui Pan and Manabu Okumura and Renhe Jiang
DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs
by Dongyuan Li, Shiyin Tan, Ying Zhang, Ming Jin, Shirui Pan, Manabu Okumura, Renhe Jiang
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new continuous state space model (SSM) called DyG-Mamba for dynamic graph learning. This approach aims to uncover evolutionary laws in real-world systems, enabling accurate social recommendation or early detection of cancer cells. Building upon the success of state space models like Mamba in language modeling, the authors develop DyG-Mamba by utilizing irregular time spans as control signals for SSM. This leads to significant robustness and generalization properties. The model achieves state-of-the-art performance on most datasets for dynamic link prediction and node classification tasks while demonstrating improved computation and memory efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to analyze real-world systems, like social networks or cancer cells, by learning from their dynamics over time. It uses a special type of model that can capture long-term dependencies, inspired by language modeling techniques. The key innovation is using irregular time intervals as control signals for the model, which makes it more robust and better at generalizing to new situations. The approach performs well on many datasets and could have important applications in fields like medicine or social media. |
Keywords
» Artificial intelligence » Classification » Generalization
