Xingquan Zhu

Latest

• 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