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Compact Graph Structure Learning via Mutual Information Compression. (arXiv:2201.05540v1 [cs.LG])
Jan. 17, 2022, 2:10 a.m. | Nian Liu, Xiao Wang, Lingfei Wu, Yu Chen, Xiaojie Guo, Chuan Shi
cs.LG updates on arXiv.org arxiv.org
Graph Structure Learning (GSL) recently has attracted considerable attentions
in its capacity of optimizing graph structure as well as learning suitable
parameters of Graph Neural Networks (GNNs) simultaneously. Current GSL methods
mainly learn an optimal graph structure (final view) from single or multiple
information sources (basic views), however the theoretical guidance on what is
the optimal graph structure is still unexplored. In essence, an optimal graph
structure should only contain the information about tasks while compress
redundant noise as much …
More from arxiv.org / cs.LG updates on arXiv.org
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