27 citations · 40 across the 32 of their papers we have counts for
7 papers · 1 filter
Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs
Fabrizio Frasca, Fabian Jogl, Moshe Eliasof +4
To develop a preliminary understanding towards Graph Foundation Models, we study the extent to which pretrained Graph Neural Networks can be applied across datasets, an effort requ…
Foldable SuperNets: Scalable Merging of Transformers with Different Initializations and Tasks
Edan Kinderman, Itay Hubara, Haggai Maron +1
Recent methods aim to merge neural networks (NNs) with identical architectures trained on different tasks into a single multi-task model. While most works focus on the simpler setu…
Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity
Yam Eitan, Yoav Gelberg, Guy Bar-Shalom +3
Topological deep learning (TDL) is a rapidly growing field that seeks to leverage topological structure in data and facilitate learning from data supported on topological objects,…
A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening
Guy Bar-Shalom, Yam Eitan, Fabrizio Frasca +1
Subgraph GNNs enhance message-passing GNNs expressivity by representing graphs as sets of subgraphs, demonstrating impressive performance across various tasks. However, their scala…
GRANOLA: Adaptive Normalization for Graph Neural Networks
Moshe Eliasof, Beatrice Bevilacqua, Carola-Bibiane Schönlieb +1
In recent years, significant efforts have been made to refine the design of Graph Neural Network (GNN) layers, aiming to overcome diverse challenges, such as limited expressive pow…
Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products
Guy Bar-Shalom, Beatrice Bevilacqua, Haggai Maron
In the realm of Graph Neural Networks (GNNs), two exciting research directions have recently emerged: Subgraph GNNs and Graph Transformers. In this paper, we propose an architectur…
Improved Generalization of Weight Space Networks via Augmentations
Aviv Shamsian, Aviv Navon, David W. Zhang +4
Learning in deep weight spaces (DWS), where neural networks process the weights of other neural networks, is an emerging research direction, with applications to 2D and 3D neural f…