12 citations · 37 across the 14 of their papers we have counts for
12 papers · 1 filter
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges
Sitao Luan, Chenqing Hua, Qincheng Lu +11
Homophily principle, \ie{} nodes with the same labels or similar attributes are more likely to be connected, has been commonly believed to be the main reason for the superiority of…
Towards Understanding Sensitive and Decisive Patterns in Explainable AI: A Case Study of Model Interpretation in Geometric Deep Learning
Jiajun Zhu, Siqi Miao, Rex Ying +1
The interpretability of machine learning models has gained increasing attention, particularly in scientific domains where high precision and accountability are crucial. This resear…
Efficient High-Resolution Time Series Classification via Attention Kronecker Decomposition
Aosong Feng, Jialin Chen, Juan Garza +5
The high-resolution time series classification problem is essential due to the increasing availability of detailed temporal data in various domains. To tackle this challenge effect…
Representation Learning for Frequent Subgraph Mining
Rex Ying, Tianyu Fu, Andrew Wang +3
Identifying frequent subgraphs, also called network motifs, is crucial in analyzing and predicting properties of real-world networks. However, finding large commonly-occurring moti…
Dirichlet Energy Enhancement of Graph Neural Networks by Framelet Augmentation
Jialin Chen, Yuelin Wang, Cristian Bodnar +3
Graph convolutions have been a pivotal element in learning graph representations. However, recursively aggregating neighboring information with graph convolutions leads to indistin…
Generative Explanations for Graph Neural Network: Methods and Evaluations
Jialin Chen, Kenza Amara, Junchi Yu +1
Graph Neural Networks (GNNs) achieve state-of-the-art performance in various graph-related tasks. However, the black-box nature often limits their interpretability and trustworthin…