Search Results for author: Floris Geerts

Found 10 papers, 2 papers with code

Weisfeiler-Leman at the margin: When more expressivity matters

no code implementations12 Feb 2024 Billy J. Franks, Christopher Morris, Ameya Velingker, Floris Geerts

Moreover, we focus on augmenting $1$-WL and MPNNs with subgraph information and employ classical margin theory to investigate the conditions under which an architecture's increased expressivity aligns with improved generalization performance.

A Neuro-Symbolic Framework for Answering Graph Pattern Queries in Knowledge Graphs

no code implementations6 Oct 2023 Tamara Cucumides, Daniel Daza, Pablo Barceló, Michael Cochez, Floris Geerts, Juan L Reutter, Miguel Romero

We introduce a framework for answering arbitrary graph pattern queries over incomplete knowledge graphs, encompassing both cyclic queries and tree-like queries with existentially quantified leaves.

Knowledge Graphs

WL meet VC

1 code implementation26 Jan 2023 Christopher Morris, Floris Geerts, Jan Tönshoff, Martin Grohe

Secondly, when an upper bound on the graphs' order is known, we show a tight connection between the number of graphs distinguishable by the $1\text{-}\mathsf{WL}$ and GNNs' VC dimension.

Ordered Subgraph Aggregation Networks

no code implementations22 Jun 2022 Chendi Qian, Gaurav Rattan, Floris Geerts, Christopher Morris, Mathias Niepert

Numerous subgraph-enhanced graph neural networks (GNNs) have emerged recently, provably boosting the expressive power of standard (message-passing) GNNs.

Expressiveness and Approximation Properties of Graph Neural Networks

no code implementations ICLR 2022 Floris Geerts, Juan L. Reutter

We provide an elegant way to easily obtain bounds on the separation power of GNNs in terms of the Weisfeiler-Leman (WL) tests, which have become the yardstick to measure the separation power of GNNs.

Graph Learning

On the expressive power of message-passing neural networks as global feature map transformers

no code implementations17 Mar 2022 Floris Geerts, Jasper Steegmans, Jan Van den Bussche

We investigate the power of message-passing neural networks (MPNNs) in their capacity to transform the numerical features stored in the nodes of their input graphs.

Graph Neural Networks with Local Graph Parameters

1 code implementation NeurIPS 2021 Pablo Barceló, Floris Geerts, Juan Reutter, Maksimilian Ryschkov

We propose local graph parameter enabled GNNs as a framework for studying the latter kind of approaches and precisely characterize their distinguishing power, in terms of a variant of the WL test, and in terms of the graph structural properties that they can take into account.

Graph Learning

The expressive power of kth-order invariant graph networks

no code implementations23 Jul 2020 Floris Geerts

The expressive power of graph neural network formalisms is commonly measured by their ability to distinguish graphs.

Graph Neural Network

Walk Message Passing Neural Networks and Second-Order Graph Neural Networks

no code implementations16 Jun 2020 Floris Geerts

When it comes to concrete learnable graph neural network (GNN) formalisms that match 2-WL or W[$\ell$] in expressive power, we consider second-order graph neural networks that allow for non-linear layers.

Graph Neural Network

Let's Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework

no code implementations6 Apr 2020 Floris Geerts, Filip Mazowiecki, Guillermo A. Pérez

In this paper we cast neural networks defined on graphs as message-passing neural networks (MPNNs) in order to study the distinguishing power of different classes of such models.

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