Within the model-theoretic framework for supervised learning introduced ...
The recent Long-Range Graph Benchmark (LRGB, Dwivedi et al. 2022) introd...
Graph homomorphism counts, first explored by Lovász in 1967, have recent...
The k-dimensional Weisfeiler-Leman (k-WL) algorithm is a simple
combinat...
The fixed-point logic LREC= was developed by Grohe et al. (CSL 2011) in ...
We analyse the power of graph neural networks (GNNs) in terms of Boolean...
The expressivity of Graph Neural Networks (GNNs) is dependent on the
agg...
We prove new upper and lower bounds on the number of iterations the
k-di...
Recently, many works studied the expressive power of graph neural networ...
We propose a universal Graph Neural Network architecture which can be tr...
Graph neural networks (GNNs) are emerging in chemical engineering for th...
Quantifying the similarity between two graphs is a fundamental algorithm...
Fuels with high-knock resistance enable modern spark-ignition engines to...
In this paper we propose a graph neural network architecture solving the...
We initiate the study of probabilistic query evaluation under bag semant...
In recent years, algorithms and neural architectures based on the
Weisfe...
Lovász (1967) showed that two graphs G and H are isomorphic if and only
...
The Weisfeiler-Leman (WL) algorithm is a well-known combinatorial proced...
Graph neural networks (GNNs) are deep learning architectures for machine...
We study the problem of computing an embedding of the tuples of a relati...
We analyse the complexity of learning first-order definable concepts in ...
We propose CRaWl (CNNs for Random Walks), a novel neural network archite...
Statistical models of real world data typically involve continuous
proba...
We determine the structure of automorphism groups of finite graphs of bo...
Probabilistic databases (PDBs) model uncertainty in data in a quantitati...
We give an overview of recent advances on the graph isomorphism problem....
Probabilistic databases (PDBs) model uncertainty in data. The current
st...
Graph neural networks (GNNs) are effective models for representation lea...
A common interpretation of soft constraints penalizes the database for e...
Probabilistic databases (PDBs) are probability spaces over database
inst...
We systematically evaluate the (in-)stability of state-of-the-art node
e...
We prove that there is a graph isomorphism test running in time
n^polylo...
Vector representations of graphs and relational structures, whether
hand...
We introduce the framework of Deep Weisfeiler Leman algorithms (DeepWL),...
We prove that graphs G, G' satisfy the same sentences of first-order log...
Arguing for the need to combine declarative and probabilistic programmin...
Constraint satisfaction problems form an important and wide class of
com...
The Weisfeiler-Leman (WL) dimension of a graph is a measure for the inhe...
Probabilistic databases (PDBs) are used to model uncertainty in data in ...
We prove that the combinatorial Weisfeiler-Leman algorithm of dimension
...
In recent years, graph neural networks (GNNs) have emerged as a powerful...
Probabilistic databases (PDBs) introduce uncertainty into relational
dat...
We give a new fpt algorithm testing isomorphism of n-vertex graphs of tr...
We prove that for every positive integer k, there exists an
MSO_1-transd...
We establish new, and surprisingly tight, connections between propositio...
In this paper, we relate a beautiful theory by Lovász with a popular
heu...
The graph similarity problem, also known as approximate graph isomorphis...
Luks's algorithm (JCSS 1982) to test isomorphism of bounded degree graph...
Luks' algorithm (JCSS 1982) to test isomorphism of bounded degree graphs...
Many important combinatorial problems can be modeled as constraint
satis...