140 citations · 223 across the 10 of their papers we have counts for
10 papers
Explaining and Adapting Graph Conditional Shift
Qi Zhu, Yizhu Jiao, Natalia Ponomareva +2
Graph Neural Networks (GNNs) have shown remarkable performance on graph-structured data. However, recent empirical studies suggest that GNNs are very susceptible to distribution sh…
Domain Generalization Deep Graph Transformation
Shiyu Wang, Guangji Bai, Qingyang Zhu +2
Graph transformation that predicts graph transition from one mode to another is an important and common problem. Despite much progress in developing advanced graph transformation t…
Single-Pass Contrastive Learning Can Work for Both Homophilic and Heterophilic Graph
Haonan Wang, Jieyu Zhang, Qi Zhu +3
Existing graph contrastive learning (GCL) techniques typically require two forward passes for a single instance to construct the contrastive loss, which is effective for capturing…
Augmentation-Free Graph Contrastive Learning with Performance Guarantee
Haonan Wang, Jieyu Zhang, Qi Zhu +1
Graph contrastive learning (GCL) is the most representative and prevalent self-supervised learning approach for graph-structured data. Despite its remarkable success, existing GCL…
Shift-Robust Node Classification via Graph Adversarial Clustering
Qi Zhu, Chao Zhang, Chanyoung Park +2
Graph Neural Networks (GNNs) are de facto node classification models in graph structured data. However, during testing-time, these algorithms assume no data shift, i.e., $\Pr_\text…
Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data
Qi Zhu, Natalia Ponomareva, Jiawei Han +1
There has been a recent surge of interest in designing Graph Neural Networks (GNNs) for semi-supervised learning tasks. Unfortunately this work has assumed that the nodes labeled f…
Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization
Qi Zhu, Carl Yang, Yidan Xu +3
Graph neural networks (GNNs) have achieved superior performance in various applications, but training dedicated GNNs can be costly for large-scale graphs. Some recent work started…
Unsupervised Differentiable Multi-aspect Network Embedding
Chanyoung Park, Carl Yang, Qi Zhu +3
Network embedding is an influential graph mining technique for representing nodes in a graph as distributed vectors. However, the majority of network embedding methods focus on lea…
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks
Yu Shi, Qi Zhu, Fang Guo +2
Heterogeneous information networks (HINs) are ubiquitous in real-world applications. In the meantime, network embedding has emerged as a convenient tool to mine and learn from netw…
Integrating Local Context and Global Cohesiveness for Open Information Extraction
Qi Zhu, Xiang Ren, Jingbo Shang +3
Extracting entities and their relations from text is an important task for understanding massive text corpora. Open information extraction (IE) systems mine relation tuples (i.e.,…