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Found 28 papers, 9 papers with code
Are Targeted Messages More Effective?
In the first version, a message only depends on the state of the source vertex, whereas in the second version it depends on the states of the source and target vertices.
Future Directions in Foundations of Graph Machine Learning
Machine learning on graphs, especially using graph neural networks (GNNs), has seen a surge in interest due to the wide availability of graph data across a broad spectrum of disciplines, from life to social and engineering sciences.
Selecting Walk Schemes for Database Embedding
We study the embedding of the tuples of a relational database, where existing techniques are often based on optimization tasks over a collection of random walks from the database.
Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark
The recent Long-Range Graph Benchmark (LRGB, Dwivedi et al. 2022) introduced a set of graph learning tasks strongly dependent on long-range interaction between vertices.
Ranked #1 on
Link Prediction
on PCQM-Contact
(MRR-ext-filtered metric)
Structural Node Embeddings with Homomorphism Counts
Grohe (PODS 2020) proposed the theoretical foundations for using homomorphism counts in machine learning on graph level as well as node level tasks.
The Descriptive Complexity of Graph Neural Networks
We prove that the graph queries that can be computed by a polynomial-size bounded-depth family of GNNs are exactly those definable in the guarded fragment GFO+C of first-order logic with counting and with built-in relations.
Some Might Say All You Need Is Sum
We prove that under certain restrictions, every Mean or Max GNN can be approximated by a Sum GNN, but even there, a combination of (Sum, [Mean/Max]) is more expressive than Sum alone.
WL meet VC
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.
One Model, Any CSP: Graph Neural Networks as Fast Global Search Heuristics for Constraint Satisfaction
We propose a universal Graph Neural Network architecture which can be trained as an end-2-end search heuristic for any Constraint Satisfaction Problem (CSP).
Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction
We recommend using sum pooling for the prediction of properties that depend on molecular size and compare pooling functions for properties that are molecular size-independent.
Graph Machine Learning for Design of High-Octane Fuels
We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space.
Solving AC Power Flow with Graph Neural Networks under Realistic Constraints
In our approach, we demonstrate the development of a framework that uses graph neural networks to learn the physical constraints of the power flow.
Weisfeiler and Leman go Machine Learning: The Story so far
In recent years, algorithms and neural architectures based on the Weisfeiler--Leman algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a powerful tool for machine learning with graphs and relational data.
The Logic of Graph Neural Networks
Graph neural networks (GNNs) are deep learning architectures for machine learning problems on graphs.
Stable Tuple Embeddings for Dynamic Databases
We study the problem of computing an embedding of the tuples of a relational database in a manner that is extensible to dynamic changes of the database.
Knowledge Graphs
Databases
On the Parameterized Complexity of Learning First-Order Logic
We analyse the complexity of learning first-order queries in a model-theoretic framework for supervised learning introduced by (Grohe and Tur\'an, TOCS 2004).
Logic in Computer Science
Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing
As the theoretical basis for our approach, we prove a theorem stating that the expressiveness of CRaWl is incomparable with that of the Weisfeiler Leman algorithm and hence with graph neural networks.
Ranked #1 on
Graph Classification
on REDDIT-B
Probabilistic Data with Continuous Distributions
In (Grohe, Kaminski, Katoen, Lindner; PODS 2020) we extend the declarative probabilistic programming language Generative Datalog, proposed by (B\'ar\'any et al.~2017) for discrete probability distributions, to continuous probability distributions and show that such programs yield generative models of continuous probabilistic databases.
Probabilistic Programming
Databases
Automorphism groups of graphs of bounded Hadwiger number
The first one states that the order of non-alternating, non-abelian composition factors for automorphism groups of graphs of bounded Hadwiger number is bounded.
Combinatorics Discrete Mathematics Group Theory 05C75, 05C83, 20D60
Recent Advances on the Graph Isomorphism Problem
We give an overview of recent advances on the graph isomorphism problem.
Data Structures and Algorithms Discrete Mathematics Combinatorics 05C85 F.2.2; G.2.2
The Surprising Power of Graph Neural Networks with Random Node Initialization
In this work, we analyze the expressive power of GNNs with RNI, and prove that these models are universal, a first such result for GNNs not relying on computationally demanding higher-order properties.
The Effects of Randomness on the Stability of Node Embeddings
We systematically evaluate the (in-)stability of state-of-the-art node embedding algorithms due to randomness, i. e., the random variation of their outcomes given identical algorithms and graphs.
word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings of Structured Data
Vector representations of graphs and relational structures, whether hand-crafted feature vectors or learned representations, enable us to apply standard data analysis and machine learning techniques to the structures.
Graph Neural Networks for Maximum Constraint Satisfaction
Many combinatorial optimization problems can be phrased in the language of constraint satisfaction problems.
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
We show that GNNs have the same expressiveness as the $1$-WL in terms of distinguishing non-isomorphic (sub-)graphs.
Ranked #4 on
Graph Classification
on NCI1
Learning MSO-definable hypotheses on string
We study the classification problems over string data for hypotheses specified by formulas of monadic second-order logic MSO.
Learning first-order definable concepts over structures of small degree
We consider a declarative framework for machine learning where concepts and hypotheses are defined by formulas of a logic over some background structure.
Dimension Reduction via Colour Refinement
We demonstrate empirically that colour refinement can indeed greatly reduce the cost of solving linear programs.
