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Found 5 papers, 4 papers with code
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)
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).
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
