SHEAF NEURAL NETWORKS WITH CONNECTION LAPLACIANS

[2206.08702] Sheaf Neural Networks with Connection Laplacians

A Sheaf Neural Network (SNN) is a type of Graph Neural Network (GNN) that operates on a sheaf, an object that equips a graph with vector spaces over its nodes ...

A Sheaf Neural Network (SNN) is a type of Graph Neural Network (GNN) that operates on a sheaf, an object that equips a graph with vector spaces over its ...

arXiv:2206.08702v1 [cs.LG] 17 Jun 2022

achieved by moving the computation of the sheaf Laplacian. Page 6. Sheaf Neural Networks with Connection Laplacians. Table 1. Accuracy ...

[PDF] Sheaf Neural Networks | Semantic Scholar

The sheaf Laplacian and associated matrices provide an extended version of the diffusion operation in graph convolutional networks, providing a proper ...

antoniopurificato/Sheaf4Rec - GitHub

... Sheaf Neural Networks with Connection Laplacians. @misc{barbero2022sheaf, title={Sheaf Neural Networks with Connection Laplacians}, author={Federico Barbero ...

Sheaf Neural Networks with Connection Laplacians - ResearchGate

Abstract and Figures. A Sheaf Neural Network (SNN) is a type of Graph Neural Network (GNN) that operates on a sheaf, an object that equips a ...

Sheaf Neural Networks - OpenReview

These sheaf neural networks are based on the sheaf Laplacian, a generalization of the ... connection Laplacians [9] and matrix-weighted Laplacians [10]. In this ...

Sheaf Neural Networks with Connection Laplacians by Federico ...

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Sheaf Neural Networks - ResearchGate

These sheaf neural networks are based on the sheaf Laplacian, a ... Vector Diffusion Maps and the Connection Laplacian. Article. Aug 2012 ...

Attention-based Sheaf Neural Networks

For this reason, the sheaf Laplacian in the case of O(d) restriction maps is also called the connection Laplacian (Singer and Wu, 2012), due to its relationship ...

publications | Cristian Bodnar

Sheaf Attention Networks. Federico Barbero, Cristian Bodnar, Haitz Sáez ... Sheaf Neural Networks with Connection Laplacians. Federico Barbero, Cristian ...

Sheaf Diffusion Goes Nonlinear: Enhancing GNNs with Adaptive ...

We introduce a general and data-dependent nonlinearity into the Laplacian of Sheaf Neural Networks, that enhances message propagation.

Sheaf-based Positional Encodings for Graph Neural Networks

Sheaf Neural Networks with Connection Laplacians, 2022. [2] Cristian Bodnar, Francesco Di Giovanni, Benjamin Paul Chamberlain, Pietro Liò, and Michael M ...

Lecture 3: Sheaf Neural Networks - Cristian Bodnar - YouTube

Video recording of the First Italian Summer School on Geometric Deep Learning, which took place in July 2022 in Pescara.

lrnzgiusti/awesome-topological-deep-learning - GitHub

Simplicial Convolutional Neural Networks. Maosheng Yang, Elvin Isufi, Geert Leus. ICASSP 2022. Paper, Code. Sheaf Neural Networks with Connection Laplacians.

Topological Deep Learning - Part 2: Sheaf Neural Networks

10Singer and Wu, “Vector diffusion maps and the connection Laplacian”, 2012. 73 / 109. Page 74. The Expressive Power of Sheaf Diffusion.

Neural Sheaf Diffusion for deep learning on graphs

Graph Neural Networks (GNNs) are connected to diffusion equations that exchange information between the nodes of a graph.

Robert Ghrist (5/1/21): Laplacians and Network Sheaves - YouTube

This talk will begin with a simple introduction to cellular sheaves as a generalized notion of a network of algebraic objects.

A Sheaf-based Approach to Graph Neural Networks - YouTube

The multitude of applications where data is attached to spaces with non-Euclidean structure has driven the rise of the field of Geometric ...

papers | Jakob Hansen

This paper is a brief exploration of the use of sheaf Laplacians as building blocks for graph neural networks.