Mar 21, 2020 · The dual architecture (micro- and marco-architectures) optimisation allows PDNAS to find deeper GNNs on diverse datasets with better performance ...
The proposed Probabilistic Dual Network Architecture Search (PDNAS) framework for GNNs not only optimises the operations within a single graph block, ...
This is a curated list of must-read papers on efficient Graph Neural Networks and scalable Graph Representation Learning for real-world applications.
Mar 21, 2020 · We propose the first probabilistic dual network archi- tecture search (PDNAS) method for GNNs. The pro- posed method uses Gumbel-sigmoid to ...
Dec 4, 2023 · Neural architecture search automates the design of neural network architectures usually by exploring a large and thus complex architecture search space.
Multimodal graph neural architecture search (MGNAS) has shown great success for automatically designing the optimal multimodal graph neural network (MGNN) ...
Aug 18, 2023 · We propose PAS (Pooling Architecture Search) to design adaptive pooling architectures by using the neural architecture search (NAS).
It adds an embedding guidance module to a discrete graph diffusion model, allowing for the generation of graphs with similar structures to a given graph.
Mar 1, 2023 · This paper proposes an Efficient Graph Neural Architecture Search (EGNAS) method based on Monte Carlo Tree Search (MCTS) and a prediction network.
Graph neural architecture search has shown great potentials for automatically designing graph neu- ral network (GNN) architectures for graph classifi- cation ...
Missing: Probabilistic Dual