expressive power

What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding NeurIPS 2023 A Theoretical Comparison of Graph Neural Network Extensions ICML 2022 Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks NeurIPS 2022 How Powerful are K-hop Message Passing Graph Neural Networks NeurIPS 2022 How Powerful are Spectral Graph Neural Networks ICML 2022 On Scrambling Phenomena for Randomly Initialized Recurrent Networks NeurIPS 2022 Ordered Subgraph Aggregation Networks NeurIPS 2022 Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries NeurIPS 2022 Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power NeurIPS 2022 Width is Less Important than Depth in ReLU Neural Networks COLT 2022 Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective ICLR 2021 Expressive Power of Invariant and Equivariant Graph Neural Networks ICLR 2021 Identity-aware Graph Neural Networks AAAI 2021 On Graph Neural Networks versus Graph-Augmented MLPs ICLR 2021 Successor-Invariant First-Order Logic on Classes of Bounded Degree (Extended Abstract) IJCAI 2021 Can Graph Neural Networks Count Substructures? NeurIPS 2020 Complexity and Expressive Power of Disjunction and Negation in Limit Datalog AAAI 2020 On the Expressive Power of Kernel Methods and the Efficiency of Kernel Learning by Association Schemes ALT 2020 On the Expressivity of ASK Queries in SPARQL AAAI 2020 The Expressive Power of a Class of Normalizing Flow Models AISTATS 2020 What graph neural networks cannot learn: depth vs width ICLR 2020 DatalogMTL: Computational Complexity and Expressive Power IJCAI 2019 Expressive power of tensor-network factorizations for probabilistic modeling NeurIPS 2019 How Powerful are Graph Neural Networks? ICLR 2019 On the Expressive Power of Deep Polynomial Neural Networks NeurIPS 2019