mlsanityData to 7 Oct 2026

MarkovGNN: Graph Neural Networks on Markov Diffusion

arXiv 2202.02470 ↗First posted 5 Feb 2022cs.LG cs.AIcited by 1

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Abstract (arXiv)

Most real-world networks contain well-defined community structures where nodes are densely connected internally within communities. To learn from these networks, we develop MarkovGNN that captures the formation and evolution of communities directly in different convolutional layers. Unlike most Graph Neural Networks (GNNs) that consider a static graph at every layer, MarkovGNN generates different stochastic matrices using a Markov process and then uses these community-capturing matrices in different layers. MarkovGNN is a general approach that could be used with most existing GNNs. We experimentally show that MarkovGNN outperforms other GNNs for clustering, node classification, and visualization tasks. The source code of MarkovGNN is publicly available at urlhttps://github.com/HipGraph/MarkovGNN.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 29 of its 42 references that are in the map. First posted on arXiv 5 Feb 2022.

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29 of 42 references.

References · 29

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17 more
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  1. 2022Triple Sparsification of Graph Convolutional Networks without Sacrificing the Accuracycited by 4