activity
20212024
most citedGraph Self-Supervised Learning: A Survey

518 citations · 528 across the 10 of their papers we have counts for

collaborators
Showing cs.LGShow all

7 papers · 1 filter

cs.LG2024

Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for Federated Recommender Systems

Zhen Cai, Tao Tang, Shuo Yu +2

Federated recommender systems have been crucially enhanced through data sharing and continuous model updates, attributed to the pervasive connectivity and distributed computing cap…

cs.LG2023★ 3 cited

Quantum Graph Learning: Frontiers and Outlook

Shuo Yu, Ciyuan Peng, Yingbo Wang +3

Quantum theory has shown its superiority in enhancing machine learning. However, facilitating quantum theory to enhance graph learning is in its infancy. This survey investigates t…

cs.LG2022

Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges

Jing Ren, Feng Xia, Azadeh Noori Hoshyar +1

Anomaly analytics is a popular and vital task in various research contexts, which has been studied for several decades. At the same time, deep learning has shown its capacity in so…

cs.LG2022

Graph Augmentation Learning

Shuo Yu, Huafei Huang, Minh N. Dao +1

Graph Augmentation Learning (GAL) provides outstanding solutions for graph learning in handling incomplete data, noise data, etc. Numerous GAL methods have been proposed for graph-…

cs.LG2022★ 1 cited

Graph Lifelong Learning: A Survey

Falih Gozi Febrinanto, Feng Xia, Kristen Moore +2

Graph learning is a popular approach for performing machine learning on graph-structured data. It has revolutionized the machine learning ability to model graph data to address dow…

cs.LG2022

Physics-Informed Graph Learning

Ciyuan Peng, Feng Xia, Vidya Saikrishna +1

An expeditious development of graph learning in recent years has found innumerable applications in several diversified fields. Of the main associated challenges are the volume and…

cs.LG2021★ 518 cited

Graph Self-Supervised Learning: A Survey

Yixin Liu, Ming Jin, Shirui Pan +4

Deep learning on graphs has attracted significant interests recently. However, most of the works have focused on (semi-) supervised learning, resulting in shortcomings including he…