89 citations · 382 across the 16 of their papers we have counts for
9 papers · 1 filter
Learning to Count Isomorphisms with Graph Neural Networks
Xingtong Yu, Zemin Liu, Yuan Fang +1
Subgraph isomorphism counting is an important problem on graphs, as many graph-based tasks exploit recurring subgraph patterns. Classical methods usually boil down to a backtrackin…
GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks
Zemin Liu, Xingtong Yu, Yuan Fang +1
Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neur…
Link Prediction on Latent Heterogeneous Graphs
Trung-Kien Nguyen, Zemin Liu, Yuan Fang
On graph data, the multitude of node or edge types gives rise to heterogeneous information networks (HINs). To preserve the heterogeneous semantics on HINs, the rich node/edge type…
On Generalized Degree Fairness in Graph Neural Networks
Zemin Liu, Trung-Kien Nguyen, Yuan Fang
Conventional graph neural networks (GNNs) are often confronted with fairness issues that may stem from their input, including node attributes and neighbors surrounding a node. Whil…
A Survey on Spectral Graph Neural Networks
Deyu Bo, Xiao Wang, Yang Liu +3
Graph neural networks (GNNs) have attracted considerable attention from the research community. It is well established that GNNs are usually roughly divided into spatial and spectr…
On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach
Ruichu Cai, Yuxuan Zhu, Xuexin Chen +4
The explainability of Graph Neural Networks (GNNs) is critical to various GNN applications, yet it remains a significant challenge. A convincing explanation should be both necessar…
Node-wise Localization of Graph Neural Networks
Zemin Liu, Yuan Fang, Chenghao Liu +1
Graph neural networks (GNNs) emerge as a powerful family of representation learning models on graphs. To derive node representations, they utilize a global model that recursively a…
Meta-Inductive Node Classification across Graphs
Zhihao Wen, Yuan Fang, Zemin Liu
Semi-supervised node classification on graphs is an important research problem, with many real-world applications in information retrieval such as content classification on a socia…
Multi-View Collaborative Network Embedding
Sezin Kircali Ata, Yuan Fang, Min Wu +3
Real-world networks often exist with multiple views, where each view describes one type of interaction among a common set of nodes. For example, on a video-sharing network, while t…