Large-Scale Graph Neural Network for Artificial Intelligence
- 1.2 Data science
- 1.3 Artificial intelligence fundamentals
- 2.4 High performance
- 3.4 Chemistry
- 3.5 Biology
- 3.8 Informatics
Toyotaro Suzumura
Graduate School of Information Science and Technology
Professor
A graph is a simple but fundamental data structure with which many real-world applications can be efficiently modelled, such as social network, purchase network, spatial-temporal network, biological network, material science, knowledge graph, and so forth. This project focuses on new learning methods for expressing graph structures using neural network called ¡°Graph Neural Networks¡±.
Related links
Related publications
- EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs.
- Efficient Scaling of Dynamic Graph Neural Networks, ACM/IEEE Supercomputing 2021
Contact
- Toyotaro Suzumura
- Email: suzumura[at]ds.itc.u-tokyo.ac.jp
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