Search Results for author: Frederik Wenkel

Found 5 papers, 2 papers with code

Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks

no code implementations22 Jan 2022 Frederik Wenkel, Yimeng Min, Matthew Hirn, Michael Perlmutter, Guy Wolf

However, current GNN models (and GCNs in particular) are known to be constrained by various phenomena that limit their expressive power and ability to generalize to more complex graph datasets.

Towards a Taxonomy of Graph Learning Datasets

no code implementations27 Oct 2021 Renming Liu, Semih Cantürk, Frederik Wenkel, Dylan Sandfelder, Devin Kreuzer, Anna Little, Sarah McGuire, Leslie O'Bray, Michael Perlmutter, Bastian Rieck, Matthew Hirn, Guy Wolf, Ladislav Rampášek

Graph neural networks (GNNs) have attracted much attention due to their ability to leverage the intrinsic geometries of the underlying data.

Graph Learning

Geometric Scattering Attention Networks

1 code implementation28 Oct 2020 Yimeng Min, Frederik Wenkel, Guy Wolf

Geometric scattering has recently gained recognition in graph representation learning, and recent work has shown that integrating scattering features in graph convolution networks (GCNs) can alleviate the typical oversmoothing of features in node representation learning.

Graph Representation Learning Node Classification

Data-Driven Learning of Geometric Scattering Networks

no code implementations6 Oct 2020 Alexander Tong, Frederik Wenkel, Kincaid MacDonald, Smita Krishnaswamy, Guy Wolf

We propose a new graph neural network (GNN) module, based on relaxations of recently proposed geometric scattering transforms, which consist of a cascade of graph wavelet filters.

Graph Classification

Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks

1 code implementation NeurIPS 2020 Yimeng Min, Frederik Wenkel, Guy Wolf

Graph convolutional networks (GCNs) have shown promising results in processing graph data by extracting structure-aware features.

Graph Attention Node Classification

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