On the expressive power of query languages for matrices
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Abstract (arXiv)
We investigate the expressive power of mathsfMATLANG, a formal language for matrix manipulation based on common matrix operations and linear algebra. The language can be extended with the operation mathsfinv of inverting a matrix. In mathsfMATLANG+mathsfinv we can compute the transitive closure of directed graphs, whereas we show that this is not possible without inversion. Indeed we show that the basic language can be simulated in the relational algebra with arithmetic operations, grouping, and summation. We also consider an operation mathsfeigen for diagonalizing a matrix, which is defined so that different eigenvectors returned for a same eigenvalue are orthogonal. We show that mathsfinv can be expressed in mathsfMATLANG+mathsfeigen. We put forward the open question whether there are boolean queries about matrices, or generic queries about graphs, expressible in mathsfMATLANG + mathsfeigen but not in mathsfMATLANG+mathsfinv. The evaluation problem for mathsfMATLANG + mathsfeigen is shown to be complete for the complexity class exists R.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Sep 2017.
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Citing papers per year: 2021: 2; 2022: 6; 2023: 3; 2024: 1; 2025: 1; 2026: 1.
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- 2021Breaking the Limits of Message Passing Graph Neural Networkscited by 101
- 2022Expressiveness and Approximation Properties of Graph Neural Networkscited by 63
- 2023Equivariant Polynomials for Graph Neural Networkscited by 38
- 2022Going Deeper into Permutation-Sensitive Graph Neural Networkscited by 15
- 2022On the expressive power of message-passing neural networks as global feature map transformerscited by 8
- 2021Functional Collection Programming with Semi-Ring Dictionariescited by 6
- 2023State of the Art and Potentialities of Graph-level Learningcited by 6
- 2022Incorporating Heterophily into Graph Neural Networks for Graph Classificationcited by 4
- 2025A Logical View of GNN-Style Computation and the Role of Activation Functionscited by 3
- 2022Beyond 1-WL with Local Ego-Network Encodingscited by 2
- 2026Recursive querying of neural networks via weighted structurescited by 2
- 2022ToL: A Tensor of List-Based Unified Computation Modelcited by 0
- 2026Recursive querying of neural networks via weighted structures6 Jan 2026
- 2025A Logical View of GNN-Style Computation and the Role of Activation Functions22 Dec 2025
- 2024Finding path and cycle counting formulae in graphs with Deep Reinforcement Learning2 Oct 2024
- 2023Technical report: Graph Neural Networks go Grammatical2 Mar 2023
- 2023Equivariant Polynomials for Graph Neural Networks22 Feb 2023
- 2023State of the Art and Potentialities of Graph-level Learning14 Jan 2023
- 2022ToL: A Tensor of List-Based Unified Computation Model21 Dec 2022
- 2022Beyond 1-WL with Local Ego-Network Encodings27 Nov 2022
- 2022Going Deeper into Permutation-Sensitive Graph Neural Networks28 May 2022
- 2022Expressiveness and Approximation Properties of Graph Neural Networks10 Apr 2022
- 2022On the expressive power of message-passing neural networks as global feature map transformers17 Mar 2022
- 2022Incorporating Heterophily into Graph Neural Networks for Graph Classification15 Mar 2022
2 more
- 2021Breaking the Limits of Message Passing Graph Neural Networkscited by 101
- 2022Expressiveness and Approximation Properties of Graph Neural Networkscited by 63
- 2023Equivariant Polynomials for Graph Neural Networkscited by 38
- 2022Going Deeper into Permutation-Sensitive Graph Neural Networkscited by 15
- 2022On the expressive power of message-passing neural networks as global feature map transformerscited by 8
- 2021Functional Collection Programming with Semi-Ring Dictionariescited by 6
- 2023State of the Art and Potentialities of Graph-level Learningcited by 6
- 2022Incorporating Heterophily into Graph Neural Networks for Graph Classificationcited by 4
- 2025A Logical View of GNN-Style Computation and the Role of Activation Functionscited by 3
- 2022Beyond 1-WL with Local Ego-Network Encodingscited by 2
- 2026Recursive querying of neural networks via weighted structurescited by 2
- 2022ToL: A Tensor of List-Based Unified Computation Modelcited by 0
12 most cited of 14 All 14 (JSON)
- 2026Recursive querying of neural networks via weighted structures6 Jan 2026
- 2025A Logical View of GNN-Style Computation and the Role of Activation Functions22 Dec 2025
- 2024Finding path and cycle counting formulae in graphs with Deep Reinforcement Learning2 Oct 2024
- 2023Technical report: Graph Neural Networks go Grammatical2 Mar 2023
- 2023Equivariant Polynomials for Graph Neural Networks22 Feb 2023
- 2023State of the Art and Potentialities of Graph-level Learning14 Jan 2023
- 2022ToL: A Tensor of List-Based Unified Computation Model21 Dec 2022
- 2022Beyond 1-WL with Local Ego-Network Encodings27 Nov 2022
- 2022Going Deeper into Permutation-Sensitive Graph Neural Networks28 May 2022
- 2022Expressiveness and Approximation Properties of Graph Neural Networks10 Apr 2022
- 2022On the expressive power of message-passing neural networks as global feature map transformers17 Mar 2022
- 2022Incorporating Heterophily into Graph Neural Networks for Graph Classification15 Mar 2022
12 newest of 14 All 14 (JSON)