Spectral Filtering Using Periodic Autoregressive Moving Average Graph Neural Networks for Heterophilic Graphs

The importance of Graph Neural Networks (GNNs) has increased over the years due to their ability to handle non-Euclidean data. Most of the existing research mainly focuses on spatial relationships using neighboring nodes to aggregate information, which usually corresponds to lowpass filtering from t...

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Hlavní autoři:	Baimyrza Kalmyrzayev, Hashim Ali, Muhammad Tahir Akhtar
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	IEEE 2026-01-01
Edice:	IEEE Access
Témata:	Autoregressive moving average filter graph neural networks graph signal processing highpass filtering spectral graph neural networks
On-line přístup:	https://ieeexplore.ieee.org/document/11357922/
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