In this half-day tutorial, we examine the state-of-the-art in GNNs and introduce a comprehensive framework that bridges the spatial and spectral domains, ...

Intro · Tentitative Program overview

Oct 21, 2024 · The tutorial delves into key paradigms, such as spatial and spectral methods, through a synthesis of spectral graph theory and approximation ...

The purpose of this study is to establish a unified framework that integrates GNNs based on spectral graph and approximation theory.

In this paper, we will present the novel neural network method for directed hypergraph. In the other words, we will develop not only the novel directed ... [ ...

CIKM 24' Tutorial - Unifying Spectral and Spatial Graph Neural Networks · How to unify spectral and spatial graph neural networks. Last updated on Jul 18, 2024.

Sep 27, 2024 · This paper proposes the Spectral Spatio-Temporal Graph Neural Network (S2GNN) to address these challenges, unifying short- and long-sequence spatio-temporal ...

Jul 21, 2021 · Graph neural networks (GNNs) are designed to deal with graph-structural data that classical deep learning does not easily manage. Since most ...

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