Post #532

Fresh picks from ArXiv This week on ArXiv: analysis of transformers, resolving scalability, and new attacks ⚔️ If I forgot to mention your paper, please shoot me a message and I will update the post. Embeddings * Self-supervised Graph-level Representation Learning with Local and Global Structure with Jian Tang * Do Transformers Really Perform Bad for Graph Representation? * Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation ICML 2021 * Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach ICML 2021 GNNs * TDGIA:Effective Injection Attacks on Graph Neural Networks KDD 2021 * Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction with Jian Tang * Is Homophily a Necessity for Graph Neural Networks? * Learning to Pool in Graph Neural Networks for Extrapolation * GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings with Jure Leskovec * Scaling Up Graph Neural Networks Via Graph Coarsening * Rethinking Graph Transformers with Spectral Attention with William L. Hamilton * Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns * Breaking the Limits of Message Passing Graph Neural Networks Survey * Survey of Image Based Graph Neural Networks * Graph Neural Networks for Natural Language Processing: A Survey