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Xingquan Zhu
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Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data
Attraction and Repulsion: Unsupervised Domain Adaptive Graph Contrastive Learning Network
Deep Learning Data Augmentation for Raman Spectroscopy Cancer Tissue Classification
OpenWGL: Open-World Graph Learning for Unseen Class Node Classification
Learning Graph Neural Networks with Positive and Unlabeled Nodes
OpenWGL: Open-World Graph Learning
Unsupervised Domain Adaptive Graph Convolutional Networks
Domain-Adversarial Graph Neural Networks for Text Classification
Relation Structure-Aware Heterogeneous Graph Neural Network
CFOND: consensus factorization for co-clustering networked data
Hashing for adaptive real-time graph stream classification with concept drifts
Multi-instance learning with discriminative bag mapping
Multiple structure-view learning for graph classification
Boosting for graph classification with universum
MGAE: marginalized graph autoencoder for graph clustering
Positive and unlabeled multi-graph learning
Task sensitive feature exploration and learning for multitask graph classification
Co-clustering enterprise social networks
Direct discriminative bag mapping for multi-instance learning
Joint structure feature exploration and regularization for multi-task graph classification
Multi-graph-view subgraph mining for graph classification
SODE: Self-adaptive one-dependence estimators for classification
Tri-party deep network representation
Boosting for multi-graph classification
CogBoost: boosting for fast cost-sensitive graph classification
Finding the best not the most: regularized loss minimization subgraph selection for graph classification
Graph ensemble boosting for imbalanced noisy graph stream classification
Multi-graph-view learning for complicated object classification
Multi-graph-view Learning for Graph Classification
Self-adaptive attribute weighting for Naive Bayes classification
Attribute weighting: how and when does it work for Bayesian Network Classification
Dual instance and attribute weighting for Naive Bayes classification
Exploring features for complicated objects: cross-view feature selection for multi-instance learning
Multi-graph learning with positive and unlabeled bags
Graph classification with imbalanced class distributions and noise
Graph stream classification using labeled and unlabeled graphs
CGStream: Continuous correlated graph query for data streams
Continuous top-k query for graph streams
Top-k correlated subgraph query for data streams
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