Chengqi Zhang

Latest

• Beyond low-pass filtering: Graph convolutional networks with automatic filtering
• Deep Neighbor-aware Embedding for Node Clustering in Attributed Graphs
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
• A comprehensive survey on graph neural networks
• Learning Graph Embedding With Adversarial Training Methods
• Attributed Graph Clustering: A Deep Attentional Embedding Approach
• Graph WaveNet for Deep Spatial-Temporal Graph Modeling
• CFOND: consensus factorization for co-clustering networked data
• Cost-sensitive parallel learning framework for insurance intelligence operation
• Social recommendation with evolutionary opinion dynamics
• Advances in processing, mining, and learning complex data: From foundations to real-world applications
• Adversarially regularized graph autoencoder for graph embedding
• Binarized attributed network embedding
• Cost-sensitive hybrid neural networks for heterogeneous and imbalanced data
• DiSAN: directional self-attention network for RNN/CNN-free language understanding
• 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
• Co-clustering enterprise social networks
• 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
• 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 stream classification using labeled and unlabeled graphs