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+#GATs
+* Graph Attention Networks(GATs) leverage masked self-attentional layers on graph-structured data. The nodes in stacked layers have different weights and they are able to attend over their
+neighborhoods’ features, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront.
+* This code used in Qlib is implemented with PyTorch by ourselves.
+* Paper: Graph Attention Networks https://arxiv.org/pdf/1710.10903.pdf
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