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Found 4 papers, 3 papers with code
Graph Neural Machine: A New Model for Learning with Tabular Data
We then propose a new machine learning model for tabular data, the so-called Graph Neural Machine (GNM), which replaces the MLP's directed acyclic graph with a nearly complete graph and which employs a synchronous message passing scheme.
Path Neural Networks: Expressive and Accurate Graph Neural Networks
We derive three different variants of the PathNN model that aggregate single shortest paths, all shortest paths and all simple paths of length up to K. We prove that two of these variants are strictly more powerful than the 1-WL algorithm, and we experimentally validate our theoretical results.
Ranked #7 on
Graph Classification
on Peptides-func
How the Move Acceptance Hyper-Heuristic Copes With Local Optima: Drastic Differences Between Jumps and Cliffs
In recent work, Lissovoi, Oliveto, and Warwicker (Artificial Intelligence (2023)) proved that the Move Acceptance Hyper-Heuristic (MAHH) leaves the local optimum of the multimodal cliff benchmark with remarkable efficiency.
Learning Parametrised Graph Shift Operators
In many domains data is currently represented as graphs and therefore, the graph representation of this data becomes increasingly important in machine learning.
