activity
20202024
most citedGraph Self-Supervised Learning: A Survey

518 citations · 1.6k across the 16 of their papers we have counts for

collaborators
Showing 2021Show all

6 papers · 1 filter

cs.CL2021

CLICKER: A Computational LInguistics Classification Scheme for Educational Resources

Swapnil Hingmire, Irene Li, Rena Kawamura +11

A classification scheme of a scientific subject gives an overview of its body of knowledge. It can also be used to facilitate access to research articles and other materials relate…

cs.CL2021

Surfer100: Generating Surveys From Web Resources, Wikipedia-style

Irene Li, Alexander Fabbri, Rina Kawamura +7

Fast-developing fields such as Artificial Intelligence (AI) often outpace the efforts of encyclopedic sources such as Wikipedia, which either do not completely cover recently-intro…

cs.LG2021★ 221 cited

Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection

Yu Zheng, Ming Jin, Yixin Liu +3

Anomaly detection from graph data has drawn much attention due to its practical significance in many critical applications including cybersecurity, finance, and social networks. Ex…

cs.LG2021★ 126 cited

Anomaly Detection in Dynamic Graphs via Transformer

Yixin Liu, Shirui Pan, Yu Guang Wang +4

Detecting anomalies for dynamic graphs has drawn increasing attention due to their wide applications in social networks, e-commerce, and cybersecurity. Recent deep learning-based a…

cs.LG2021★ 486 cited

Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning

Yixin Liu, Zhao Li, Shirui Pan +3

Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recen…

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…