  While there exists a wide variety of graph neural networks (GNN) for node
classification, only a minority of them adopt mechanisms that effectively
target noise propagation during the message-passing procedure. Additionally, a
very important challenge that significantly affects graph neural networks is
the issue of scalability which limits their application to larger graphs. In
this paper we propose our method named NODE-SELECT: an efficient graph neural
network that uses subsetting layers which only allow the best sharing-fitting
nodes to propagate their information. By having a selection mechanism within
each layer which we stack in parallel, our proposed method NODE-SELECT is able
to both reduce the amount noise propagated and adapt the restrictive sharing
concept observed in real world graphs. Our NODE-SELECT significantly
outperformed existing GNN frameworks in noise experiments and matched
state-of-the art results in experiments without noise over different benchmark
datasets.
