7 citations · 12 across the 14 of their papers we have counts for
15 papers
The Boolean Power of ReLU
Pablo Barceló, Floris Geerts, Matthias Lanzinger +2
We prove that, on finite simple undirected graphs equipped with a single Boolean node feature, the Boolean queries expressible in -MPLang, for any collection of eventually c…
AutoGrable: What Is a Good Graph for a Table?
Tamara Cucumides, Floris Geerts
Graph learning presupposes a graph, and tables and relational databases do not come with one. Applying a GNN to them requires deciding which entities become nodes, which of them to…
Latent Bridges for Multi-Table Question Answering
Simone Varriale, Tamara Cucumides, Floris Geerts +1
We introduce GRAB, a constructor-encoder-bridge pipeline for table question answering. Our method lifts relational data into an heterogeneous graph, encodes it via message passing,…
Quadratic Sums-of-Powers for Fixed-Parameter Tractable Quantum-Circuit Simulation
Alexis de Colnet, Floris Geerts, Rihan Hai +3
Strongly simulating a quantum circuit, that is, computing an output amplitude, can be done by summing the circuit's Feynman paths: a weighted count over assignments to Boolean path…
Which Algorithms Can Graph Neural Networks Learn?
Solveig Wittig, Antonis Vasileiou, Robert R. Nerem +4
In recent years, there has been growing interest in understanding neural architectures' ability to learn to execute discrete algorithms, a line of work often referred to as neural…
DeepGraphLog for Layered Neurosymbolic AI
Adem Kikaj, Giuseppe Marra, Floris Geerts +2
Neurosymbolic AI (NeSy) aims to integrate the statistical strengths of neural networks with the interpretability and structure of symbolic reasoning. However, current NeSy framewor…
Private Quantum Database
Giancarlo Gatti, Floris Geerts, Rihan Hai
Quantum databases open an exciting new frontier in data management by offering privacy guarantees that classical systems cannot match. Traditional engines tackle user privacy, whic…
Covered Forest: Fine-grained generalization analysis of graph neural networks
Antonis Vasileiou, Ben Finkelshtein, Floris Geerts +2
The expressive power of message-passing graph neural networks (MPNNs) is reasonably well understood, primarily through combinatorial techniques from graph isomorphism testing. Howe…
Towards Bridging Generalization and Expressivity of Graph Neural Networks
Shouheng Li, Floris Geerts, Dongwoo Kim +1
Expressivity and generalization are two critical aspects of graph neural networks (GNNs). While significant progress has been made in studying the expressivity of GNNs, much less i…
Quantum Data Management in the NISQ Era: Extended Version
Rihan Hai, Shih-Han Hung, Tim Coopmans +2
Quantum computing has emerged as a promising tool for transforming the landscape of computing technology. Recent efforts have applied quantum techniques to classical database chall…
Weisfeiler-Leman at the margin: When more expressivity matters
Billy J. Franks, Christopher Morris, Ameya Velingker +1
The Weisfeiler-Leman algorithm (-WL) is a well-studied heuristic for the graph isomorphism problem. Recently, the algorithm has played a prominent role in understanding the expr…
A Neuro-Symbolic Framework for Answering Graph Pattern Queries in Knowledge Graphs
Tamara Cucumides, Daniel Daza, Pablo Barceló +4
The challenge of answering graph queries over incomplete knowledge graphs is gaining significant attention in the machine learning community. Neuro-symbolic models have emerged as…
Expressiveness and Approximation Properties of Graph Neural Networks
Floris Geerts, Juan L. Reutter
Characterizing the separation power of graph neural networks (GNNs) provides an understanding of their limitations for graph learning tasks. Results regarding separation power are,…
Expressive power of linear algebra query languages
Floris Geerts, Thomas Muñoz, Cristian Riveros +1
Linear algebra algorithms often require some sort of iteration or recursion as is illustrated by standard algorithms for Gaussian elimination, matrix inversion, and transitive clos…
The expressive power of kth-order invariant graph networks
Floris Geerts
The expressive power of graph neural network formalisms is commonly measured by their ability to distinguish graphs. For many formalisms, the k-dimensional Weisfeiler-Leman (k-WL)…