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06/06/2023
Fine-grained Expressivity of Graph Neural Networks
Numerous recent works have analyzed the expressive power of message-pass...
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02/08/2023
Attending to Graph Transformers
Recently, transformer architectures for graphs emerged as an alternative...
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01/26/2023
WL meet VC
Recently, many works studied the expressive power of graph neural networ...
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11/30/2022
Weisfeiler and Leman Go Relational
Knowledge graphs, modeling multi-relational data, improve numerous appli...
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06/22/2022
Ordered Subgraph Aggregation Networks
Numerous subgraph-enhanced graph neural networks (GNNs) have emerged rec...
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05/27/2022
MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers
Mixed-integer programming (MIP) technology offers a generic way of formu...
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03/25/2022
SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks
While (message-passing) graph neural networks have clear limitations in ...
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03/04/2022
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Combinatorial optimization is a well-established area in operations rese...
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12/18/2021
Weisfeiler and Leman go Machine Learning: The Story so far
In recent years, algorithms and neural architectures based on the Weisfe...
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10/01/2021
Reconstruction for Powerful Graph Representations
Graph neural networks (GNNs) have limited expressive power, failing to r...
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05/12/2021
The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs
In recent years, algorithms and neural architectures based on the Weisfe...
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02/18/2021
Combinatorial optimization and reasoning with graph neural networks
Combinatorial optimization is a well-established area in operations rese...
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07/16/2020
TUDataset: A collection of benchmark datasets for learning with graphs
Recently, there has been an increasing interest in (supervised) learning...
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01/27/2020
Deep Graph Matching Consensus
This work presents a two-stage neural architecture for learning and refi...
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04/02/2019
Towards a practical k-dimensional Weisfeiler-Leman algorithm
The k-dimensional Weisfeiler-Leman algorithm is a well-known heuristic f...
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03/28/2019
A Survey on Graph Kernels
Graph kernels have become an established and widely-used technique for s...
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10/04/2018
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
In recent years, graph neural networks (GNNs) have emerged as a powerful...
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06/22/2018
Hierarchical Graph Representation Learning with Differentiable Pooling
Recently, graph neural networks (GNNs) have revolutionized the field of ...
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06/22/2018
Hierarchical Graph Representation Learning withDifferentiable Pooling
Recently, graph neural networks (GNNs) have revolutionized the field of ...
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03/07/2017
Global Weisfeiler-Lehman Graph Kernels
Most state-of-the-art graph kernels only take local graph properties int...
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03/02/2017
A Unifying View of Explicit and Implicit Feature Maps for Structured Data: Systematic Studies of Graph Kernels
Non-linear kernel methods can be approximated by fast linear ones using ...
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10/01/2016
