Jia Wu

Latest

• Towards Flexible and Adaptive Neural Process for Cold-Start Recommendation
• A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning
• Task-adaptive Neural Process for User Cold-Start Recommendation
• Graph Geometry Interaction Learning
• Graph Stochastic Neural Networks for Semi-supervised Learning
• Adaptive knowledge subgraph ensemble for robust and trustworthy knowledge graph completion
• IEEE access special section editorial: advanced data analytics for large-scale complex data environments
• Time series feature learning with labeled and unlabeled data
• Active discriminative network representation learning
• Advances in processing, mining, and learning complex data: From foundations to real-world applications
• FraudNE: a joint embedding approach for fraud detection
• Multi-instance learning with discriminative bag mapping
• Multiple structure-view learning for graph classification
• Boosting for graph classification with universum
• Positive and unlabeled multi-graph learning
• Task sensitive feature exploration and learning for multitask graph classification
• Towards large-scale social networks with online diffusion provenance detection
• 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
• Locally weighted learning: how and when does it work in Bayesian networks?
• Mining top-k minimal redundancy frequent patterns over uncertain databases
• 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