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Presentations I:

• Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson’s Disease
• Cgc-net: Cell graph convolutional network for grading of colorectal cancer histology images 
• Uncertainty-based Graph Convolutional Networks for Organ Segmentation Refinement

Presentations II:

• Latent-Graph Learning for Disease Prediction
• A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CT
• Graph Random Neural Networks for Semi-Supervised Learning on Graphs

Presentations III:

• Interpreting Graph Neural Networks For NLP With Differentiable Edge Masking
• On The Bottleneck of Graph Neural Networks and Its Practical Implications
• Simplifying Graph Convolutional Networks

Presentations IV:

• Temporal Graph Networks For Deep Learning on Dynamic Graphs
• Graph Meta Learning via Local Subgraphs
• Recovering Brain Structural Connectivity from Functional Connectivity via Multi-GCN Based Generative Adversarial Network

Presentations V:

• Learning Graph Embeddings for Compositional Zero-shot Learning
• Distilling Knowledge from Graph Convolutional NetworksAttacking graph convolutional networks via rewiring.
• On The Bottleneck of Graph Neural Networks and Its Practical Implications