The concept of using attention mechanisms, including attention matrices, for modeling dynamic networks is an area of active research. However, the search did not yield any papers specifically focused on using "flash attention matrices" for this purpose. Below are some relevant papers that discuss the use of attention mechanisms in modeling dynamic networks:
### Relevant Papers
1. **[Temporal motif-based attentional graph convolutional network for dynamic link prediction](https://dblp.org/rec/journals/ida/WuCZPH23) (2023)**
- **Summary**: The paper proposes a temporal motif-based attentional graph convolutional network model (TMAGCN) that is superior to state-of-the-art baselines on the dynamic link prediction task. It suggests that temporal motifs can manifest the essential dynamic mechanism of the network.
- **Citation Count**: 0
2. **[DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks](https://dblp.org/rec/conf/wsdm/SankarWGZY20) (2020)**
- **Summary**: DySAT is a neural architecture that learns node representations to capture dynamic graph structural evolution. The paper validates the effectiveness of jointly modeling structural and temporal self-attention.
- **Citation Count**: 279
3. **[Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey](https://arxiv.org/pdf/2005.07496) (2020)**
- **Summary**: This work provides a comprehensive survey of dynamic graph neural network models and establishes a foundation for dynamic networks with consistent, detailed terminology and notation.
- **Citation Count**: 130
4. **[DMGCRN: Dynamic Multi-Graph Convolution Recurrent Network for Traffic Forecasting](https://arxiv.org/pdf/2112.02264) (2021)**
- **Summary**: The paper proposes a dynamic multi-graph convolution recurrent network (DMGCRN) that can model spatial and temporal correlations simultaneously.
- **Citation Count**: 4
5. **[Global Spatiotemporal Graph Attention Network for Sea Surface Temperature Prediction](https://dblp.org/rec/journals/lgrs/GaoLYX23) (2023)**
- **Summary**: The paper proposes a global spatiotemporal graph attention network (GSTGAT) that combines graph neural networks for modeling spatial correlations and achieves superior performance.
- **Citation Count**: 0
While these papers do not specifically mention "flash attention matrices," they do indicate that attention mechanisms, including attention matrices, can be effectively used for modeling dynamic networks. Would you like to know more about any of these papers?
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