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Found 28 papers, 17 papers with code
Weisfeiler-Leman at the margin: When more expressivity matters
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.
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.
Towards Principled Graph Transformers
Graph learning architectures based on the k-dimensional Weisfeiler-Leman (k-WL) hierarchy offer a theoretically well-understood expressive power.
Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems
Recently, machine learning, particularly message-passing graph neural networks (MPNNs), has gained traction in enhancing exact optimization algorithms.
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Recently, pre-trained foundation models have enabled significant advancements in multiple fields.
Probabilistically Rewired Message-Passing Neural Networks
Message-passing graph neural networks (MPNNs) emerged as powerful tools for processing graph-structured input.
Fine-grained Expressivity of Graph Neural Networks
In particular, we characterize the expressive power of MPNNs in terms of the tree distance, which is a graph distance based on the concept of fractional isomorphisms, and substructure counts via tree homomorphisms, showing that these concepts have the same expressive power as the $1$-WL and MPNNs on graphons.
Attending to Graph Transformers
Recently, transformer architectures for graphs emerged as an alternative to established techniques for machine learning with graphs, such as (message-passing) graph neural networks.
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.
Weisfeiler and Leman Go Relational
Namely, we investigate the limitations in the expressive power of the well-known Relational GCN and Compositional GCN architectures and shed some light on their practical learning performance.
Ordered Subgraph Aggregation Networks
Numerous subgraph-enhanced graph neural networks (GNNs) have emerged recently, provably boosting the expressive power of standard (message-passing) GNNs.
MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers
Mixed-integer programming (MIP) technology offers a generic way of formulating and solving combinatorial optimization problems.
SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks
While (message-passing) graph neural networks have clear limitations in approximating permutation-equivariant functions over graphs or general relational data, more expressive, higher-order graph neural networks do not scale to large graphs.
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Combinatorial optimization is a well-established area in operations research and computer science.
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.
Reconstruction for Powerful Graph Representations
Empirically, we show how reconstruction can boost GNN's expressive power -- while maintaining its invariance to permutations of the vertices -- by solving seven graph property tasks not solvable by the original GNN.
The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs
In recent years, algorithms and neural architectures based on the Weisfeiler-Leman algorithm, a well-known heuristic for the graph isomorphism problem, emerged as a powerful tool for (supervised) machine learning with graphs and relational data.
Combinatorial optimization and reasoning with graph neural networks
Combinatorial optimization is a well-established area in operations research and computer science.
TUDataset: A collection of benchmark datasets for learning with graphs
We provide Python-based data loaders, kernel and graph neural network baseline implementations, and evaluation tools.
Deep Graph Matching Consensus
This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs.
Ranked #12 on
Entity Alignment
on DBP15k zh-en
(using extra training data)
Temporal Graph Kernels for Classifying Dissemination Processes
Hence, we confirm that taking temporal information into account is crucial for the successful classification of dissemination processes.
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
Hence, it accounts for the higher-order interactions between vertices.
Ranked #3 on
Graph Classification
on NCI109
A Survey on Graph Kernels
Graph kernels have become an established and widely-used technique for solving classification tasks on graphs.
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
Hierarchical Graph Representation Learning with Differentiable Pooling
Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction.
Ranked #1 on
Graph Classification
on REDDIT-MULTI-12K
Global Weisfeiler-Lehman Graph Kernels
Specifically, we introduce a novel graph kernel based on the $k$-dimensional Weisfeiler-Lehman algorithm.
A Unifying View of Explicit and Implicit Feature Maps of Graph Kernels
On this basis we propose exact and approximative feature maps for widely used graph kernels based on the kernel trick.
Faster Kernels for Graphs with Continuous Attributes via Hashing
While state-of-the-art kernels for graphs with discrete labels scale well to graphs with thousands of nodes, the few existing kernels for graphs with continuous attributes, unfortunately, do not scale well.
