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Chengqi Zhang
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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
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