89 citations · 382 across the 16 of their papers we have counts for
16 papers
Prompt Tuning on Graph-augmented Low-resource Text Classification
Zhihao Wen, Yuan Fang
Text classification is a fundamental problem in information retrieval with many real-world applications, such as predicting the topics of online articles and the categories of e-co…
Augmenting Low-Resource Text Classification with Graph-Grounded Pre-training and Prompting
Zhihao Wen, Yuan Fang
Text classification is a fundamental problem in information retrieval with many real-world applications, such as predicting the topics of online articles and the categories of e-co…
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…
Efficient and Error-Resilient Data Access Protocols for a Limited-Sized Quantum Random Access Memory
Zhao-Yun Chen, Cheng Xue, Yun-Jie Wang +8
Quantum Random Access Memory (QRAM) is a critical component for loading classical data into quantum computers. While constructing a practical QRAM presents several challenges, incl…
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…
TREND: TempoRal Event and Node Dynamics for Graph Representation Learning
Zhihao Wen, Yuan Fang
Temporal graph representation learning has drawn significant attention for the prevalence of temporal graphs in the real world. However, most existing works resort to taking discre…
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…
Adaptive Task Sampling for Meta-Learning
Chenghao Liu, Zhihao Wang, Doyen Sahoo +3
Meta-learning methods have been extensively studied and applied in computer vision, especially for few-shot classification tasks. The key idea of meta-learning for few-shot classif…
Recent Advances in Network-based Methods for Disease Gene Prediction
Sezin Kircali Ata, Min Wu, Yuan Fang +3
Disease-gene association through Genome-wide association study (GWAS) is an arduous task for researchers. Investigating single nucleotide polymorphisms (SNPs) that correlate with s…
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…
Truly Multi-modal YouTube-8M Video Classification with Video, Audio, and Text
Zhe Wang, Kingsley Kuan, Mathieu Ravaut +13
The YouTube-8M video classification challenge requires teams to classify 0.7 million videos into one or more of 4,716 classes. In this Kaggle competition, we placed in the top 3% o…