- Sheaf HyperNetworks for Personalized Federated Learning🔍
- Towards Sheaf Theoretic Analyses for Delay Tolerant Networking🔍
- Iulia Duta🔍
- A Survey on Hypergraph Neural Networks🔍
- A Magnetic Laplacian based Hypergraph Neural Network🔍
- On the Current State of Sheaf Theoretic Networking🔍
- Modelling and Mining Complex Networks as Hypergraphs🔍
Mathematical Representations of Knowledge Hypergraphs
🔍
Sheaf hypergraph networks
Sheaf HyperNetworks for Personalized Federated Learning
Graph hypernetworks (GHNs), constructed by combining graph neural networks (GNNs) with hypernetworks (HNs), leverage relational data across ...
Towards Sheaf Theoretic Analyses for Delay Tolerant Networking
For routing within delay tolerant networks to truly exploit this structure, a deeper structure than a graph is required. In this paper, we develop sheaves that ...
Sheaf Hypergraph Networks. I Duta, G Cassarà, F Silvestri, P Liò. Advances in Neural Information Processing Systems (NeurIPS 2023), 2023. 11, 2023. Dynamic ...
A Survey on Hypergraph Neural Networks: An In-Depth and Step-By ...
As networks of HOIs are expressed mathematically as hypergraphs, hypergraph neural networks ... Sheaf hypergraph networks. In NeurIPS. Google ...
A Magnetic Laplacian based Hypergraph Neural Network - arxiv-sanity
We employ these sheaf hypergraph Laplacians to design two categories of models: Sheaf Hypergraph Neural Networks and Sheaf Hypergraph Convolutional Networks.
On the Current State of Sheaf Theoretic Networking
From direct translations of Internet Protocol. (IP) to satellite networks to pre-planned routing structures in Contact Graph Routing (CGR), each potential ...
Modelling and Mining Complex Networks as Hypergraphs
... graph and hypergraph theory, topological data analysis, and neural networks. ... Her work has focused on knowledge graphs, applied topology, sheaf theory, deep ...
Lecture 3: Sheaf Neural Networks - Cristian Bodnar - YouTube
Go to channel · Cristian Bodnar (11/7/23): A Sheaf-based Approach to Graph Neural Networks. Applied Algebraic Topology Network•1.3K views · 1:05 ...
Mathematical Representations of Knowledge Hypergraphs
Fully Ontologized Knowledge Hypergraph Sheaves. Robert Ellis Green1,2, Miguel ... Representations of annotated networks using knowledge hypergraphs.
... hypergraph neural networks that provides both local and global explanations. ... Abstract:Sheaf Neural Networks (SNNs) naturally extend Graph Neural Networks ...
Spectral sheaf theory extends spectral graph theory to cellular sheaves, leading to sheaf Laplacians [17]. The aim of this paper is to introduce persistent ...
publications | Cristian Bodnar
On the Expressive Power of Geometric Graph Neural Networks. Chaitanya K Joshi ... Sheaf Attention Networks. Federico Barbero, Cristian Bodnar, Haitz Sáez ...
Hypergraph neural networks are a class of powerful models that leverage ... Sheaf Neural Networks (SNNs) naturally extend Graph Neural Networks (GNNs) ...
Generalizing Graph Representation Learning with Cellular Sheaves
The Graph Convolutional Neural Network (GCN) is a popular graph neural network architecture. Used primarily for node classification/regression.
Exploring Nonlinear Sheaf Diffusion in Graph Neural Networks
Exploring Nonlinear Sheaf Diffusion in Graph Neural Networks. Core Concepts. This work delves into the potential benefits of introducing a nonlinear Laplacian ...
cellular sheaves - Jakob Hansen
Sheaves should admit versions of graph-based network concepts like ... Seeking network data that might be better explained by a sheaf, networks that might benefit ...
Sheaves II. Definition (Sheaf / cellular sheaf on a graph). A sheaf assigns a finite-dimensional real vector space J(v) resp. J(e) to each ...
Attention-based Sheaf Neural Networks
Graph Neural Networks (GNNs) offer a principled way of tackling machine learning tasks over graph structures. Despite their great success, they demonstrate some ...
... Sheaf Hypergraph Networks." The paper introduces cellular sheaves to ... Sheaf Hypergraph Neural Networks and Sheaf Hypergraph Convolutional Networks.
Topological Deep Learning - Part 2: Sheaf Neural Networks
over-smoothing for graph neural networks”, 2020. 10 / 109. Page 11. The Heterophily Problem. It was remarked3 that GNNs ...