Machine learning books and papers

Fresh picks from ArXiv
This week on ArXiv: 1000-layer GNN, solutions to OGB challenge, and theory behind GNN explanations 🤔

If I forgot to mention your paper, please shoot me a message and I will update the post.


Deep GNNs
* Training Graph Neural Networks with 1000 Layers ICML 2021
* Very Deep Graph Neural Networks Via Noise Regularisation with Petar Veličković, Peter Battaglia

Heterophily
* Improving Robustness of Graph Neural Networks with Heterophily-Inspired Designs with Danai Koutra

Knowledge graphs
* Query Embedding on Hyper-relational Knowledge Graphs with Mikhail Galkin

OGB-challenge
* Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
* First Place Solution of KDD Cup 2021 & OGB Large-Scale Challenge Graph Prediction Track

Theory
* Towards a Rigorous Theoretical Analysis and Evaluation of GNN Explanations with Marinka Zitnik
* A unifying point of view on expressive power of GNNs

GNNs
* Stability of Graph Convolutional Neural Networks to Stochastic Perturbations with Alejandro Ribeiro
* TD-GEN: Graph Generation With Tree Decomposition
* Unsupervised Resource Allocation with Graph Neural Networks
* Equivariance-bridged SO(2)-Invariant Representation Learning using Graph Convolutional Network
* GemNet: Universal Directional Graph Neural Networks for Molecules with Stephan Günnemann
* Optimizing Graph Transformer Networks with Graph-based Techniques

Survey
* Systematic comparison of graph embedding methods in practical tasks
* Evaluating Modules in Graph Contrastive Learning
* A Survey on Mining and Analysis of Uncertain Graphs

@Machine_learn

www.tg-me.com/us/Machine learning books and papers/com.Machine_learn/1719

4.8K viewsJun 23, 2021 at 00:22

Fresh picks from ArXiv
This week on ArXiv: 1000-layer GNN, solutions to OGB challenge, and theory behind GNN explanations 🤔

If I forgot to mention your paper, please shoot me a message and I will update the post.


Deep GNNs
* Training Graph Neural Networks with 1000 Layers ICML 2021
* Very Deep Graph Neural Networks Via Noise Regularisation with Petar Veličković, Peter Battaglia

Heterophily
* Improving Robustness of Graph Neural Networks with Heterophily-Inspired Designs with Danai Koutra

Knowledge graphs
* Query Embedding on Hyper-relational Knowledge Graphs with Mikhail Galkin

OGB-challenge
* Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
* First Place Solution of KDD Cup 2021 & OGB Large-Scale Challenge Graph Prediction Track

Theory
* Towards a Rigorous Theoretical Analysis and Evaluation of GNN Explanations with Marinka Zitnik
* A unifying point of view on expressive power of GNNs

GNNs
* Stability of Graph Convolutional Neural Networks to Stochastic Perturbations with Alejandro Ribeiro
* TD-GEN: Graph Generation With Tree Decomposition
* Unsupervised Resource Allocation with Graph Neural Networks
* Equivariance-bridged SO(2)-Invariant Representation Learning using Graph Convolutional Network
* GemNet: Universal Directional Graph Neural Networks for Molecules with Stephan Günnemann
* Optimizing Graph Transformer Networks with Graph-based Techniques

Survey
* Systematic comparison of graph embedding methods in practical tasks
* Evaluating Modules in Graph Contrastive Learning
* A Survey on Mining and Analysis of Uncertain Graphs

@Machine_learn

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