Web: http://arxiv.org/abs/2209.10818

Sept. 23, 2022, 1:11 a.m. | Guixiang Ma, Vy Vo, Theodore Willke, Nesreen K. Ahmed

cs.LG updates on arXiv.org arxiv.org

Graph neural networks (GNNs) have been extensively used for many domains
where data are represented as graphs, including social networks, recommender
systems, biology, chemistry, etc. Recently, the expressive power of GNNs has
drawn much interest. It has been shown that, despite the promising empirical
results achieved by GNNs for many applications, there are some limitations in
GNNs that hinder their performance for some tasks. For example, since GNNs
update node features mainly based on local information, they have limited
expressive …

arxiv graph graph neural networks memory networks neural networks neuroscience perspective

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