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Combinatorial optimization and reasoning with graph neural networks
Combinatorial optimization is a well-established area in operations rese...
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TUDataset: A collection of benchmark datasets for learning with graphs
Recently, there has been an increasing interest in (supervised) learning...
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Deep Graph Matching Consensus
This work presents a two-stage neural architecture for learning and refi...
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Towards a practical k-dimensional Weisfeiler-Leman algorithm
The k-dimensional Weisfeiler-Leman algorithm is a well-known heuristic f...
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A Survey on Graph Kernels
Graph kernels have become an established and widely-used technique for s...
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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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Hierarchical Graph Representation Learning with Differentiable Pooling
Recently, graph neural networks (GNNs) have revolutionized the field of ...
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Hierarchical Graph Representation Learning withDifferentiable Pooling
Recently, graph neural networks (GNNs) have revolutionized the field of ...
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Global Weisfeiler-Lehman Graph Kernels
Most state-of-the-art graph kernels only take local graph properties int...
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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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Faster Kernels for Graphs with Continuous Attributes via Hashing
While state-of-the-art kernels for graphs with discrete labels scale wel...
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