From 27f19c1f1b442e333358bc82a9cbcc09173dd34f Mon Sep 17 00:00:00 2001 From: meng-ustc Date: Wed, 25 Nov 2020 21:11:56 +0800 Subject: [PATCH] Add README.md for GATs --- examples/benchmarks/GATs/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/benchmarks/GATs/README.md b/examples/benchmarks/GATs/README.md index a69e7d433..f432b6c5b 100644 --- a/examples/benchmarks/GATs/README.md +++ b/examples/benchmarks/GATs/README.md @@ -1,4 +1,4 @@ -#GATs +# 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.