Fresh picks from ArXiv
This week on ArXiv: uncertainty for node classification, GNNs for tabular data, and cryptocurrency graph 🤑

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

GNNs
* A Scalable AutoML Approach Based on Graph Neural Networks
* Optimizing Sparse Matrix Multiplications for Graph Neural Networks
* Graph Embedding with Hierarchical Attentive Membership WSDM 2022
* Unbiased Graph Embedding with Biased Graph Observations
* Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification with Stephan Günnemann
* Robustness of Graph Neural Networks at Scale with Stephan Günnemann
* VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization
* Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
* Nested Graph Neural Networks with Pan Li
* Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features
* Tackling Oversmoothing of GNNs with Contrastive Learning
* On the Power of Edge Independent Graph Models
* On Provable Benefits of Depth in Training Graph Convolutional Networks
* UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation CIKM'2021
* Finding a Concise, Precise, and Exhaustive Set of Near Bi-Cliques in Dynamic Graphs WSDM 2022
* How to transfer algorithmic reasoning knowledge to learn new algorithms? with Petar Velickovic and Jian Tang


Software
* retworkx: A High-Performance Graph Library for Python
* NeuroComb: Improving SAT Solving with Graph Neural Networks
* Graph? Yes! Which one? Help!

Datasets
* Towards a Taxonomy of Graph Learning Datasets
* Cryptocurrencies Activity as a Complex Network: Analysis of Transactions Graphs