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Found 7 papers, 5 papers with code
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks
While machine learning on graphs has demonstrated promise in drug design and molecular property prediction, significant benchmarking challenges hinder its further progress and relevance.
Distinguished In Uniform: Self Attention Vs. Virtual Nodes
We first clarify that this form of universality is not unique to GTs: Using the same positional encodings, also pure MPGNNs and even 2-layer MLPs are non-uniform universal approximators.
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 #2 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
