518 citations · 528 across the 10 of their papers we have counts for
10 papers
PANDORA: Deep graph learning based COVID-19 infection risk level forecasting
Shuo Yu, Feng Xia, Yueru Wang +3
COVID-19 as a global pandemic causes a massive disruption to social stability that threatens human life and the economy. Policymakers and all elements of society must deliver measu…
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…
Knowledge Graphs: Opportunities and Challenges
Ciyuan Peng, Feng Xia, Mehdi Naseriparsa +1
With the explosive growth of artificial intelligence (AI) and big data, it has become vitally important to organize and represent the enormous volume of knowledge appropriately. As…
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…
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…
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-…
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…
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…
Solving ESL Sentence Completion Questions via Pre-trained Neural Language Models
Qiongqiong Liu, Tianqiao Liu, Jiafu Zhao +6
Sentence completion (SC) questions present a sentence with one or more blanks that need to be filled in, three to five possible words or phrases as options. SC questions are widely…
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…