5 citations · 8 across the 7 of their papers we have counts for
9 papers
Foundations and Frontiers of Graph Learning Theory
Yu Huang, Min Zhou, Menglin Yang +7
Recent advancements in graph learning have revolutionized the way to understand and analyze data with complex structures. Notably, Graph Neural Networks (GNNs), i.e. neural network…
SPGNN: Recognizing Salient Subgraph Patterns via Enhanced Graph Convolution and Pooling
Zehao Dong, Muhan Zhang, Yixin Chen
Graph neural networks (GNNs) have revolutionized the field of machine learning on non-Euclidean data such as graphs and networks. GNNs effectively implement node representation lea…
Chain of Images for Intuitively Reasoning
Fanxu Meng, Haotong Yang, Yiding Wang +1
The human brain is naturally equipped to comprehend and interpret visual information rapidly. When confronted with complex problems or concepts, we use flowcharts, sketches, and di…
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network
Lecheng Kong, Jiarui Feng, Hao Liu +3
While Graph Neural Networks (GNNs) recently became powerful tools in graph learning tasks, considerable efforts have been spent on improving GNNs' structural encoding ability. A pa…
Neural Attention: Enhancing QKV Calculation in Self-Attention Mechanism with Neural Networks
Muhan Zhang
In the realm of deep learning, the self-attention mechanism has substantiated its pivotal role across a myriad of tasks, encompassing natural language processing and computer visio…
Graph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node Tasks
Hao Liu, Jiarui Feng, Lecheng Kong +3
Graph Neural Networks (GNNs) have become popular in Graph Representation Learning (GRL). One fundamental application is few-shot node classification. Most existing methods follow t…