04132021 Graph Structure Learning

04132021 Graph Structure Learning Information Guide

1. About on 04132021 Graph Structure Learning
2. Main Features
3. Developments
4. Deep Dive
5. Conclusion

About on 04132021 Graph Structure Learning

Date Presented: 08/12/2022 Speaker: Tengfei Ma, IBM T.J. Watson Research Center Abstract: Recording of my talk given at GSP Workshop in Delft, Netherlands 2024. ArXiv: Code: ... Huizhao Wang, Hikvision Research Institute Considering that each node has its own characteristics, we believe For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: Nowadays, a lot of approaches that cheat the GNN or GCN to make a mis-classification. They call the modification of For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: ...

Main Features

Developments

Learn low-dim Embeddings that encode GRAPH structure (data) : "Representation Learning" /arXiv

KDD 2023 - Graph Structure Learning via Progressive Strategy

Compact Graph Structure Learning via Mutual Information Compression

Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 6.3 - Deep Learning for Graphs

Graph Structure Learning for Robust Graph Neural Networks

Towards Unsupervised Deep Graph Structure Learning

Yuzhou Chen (10/27/21): Topological Relational Learning on Graphs

Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 8.1 - Graph Augmentation for GNNs

Xavier Bresson: "Graph Convolutional Neural Networks for Molecule Generation"

Deep Dive

Data is compiled from public records and verified media reports.

Last Updated: June 20, 2026

Conclusion

Disclaimer: Disclaimer: Details estimates are based on publicly available data, media reports, and financial analysis. Actual numbers may vary.