  In general, Graph Neural Networks(GNN) have been using a message passing
method to aggregate and summarize information about neighbors to express their
information. Nonetheless, previous studies have shown that the performance of
graph neural networks becomes vulnerable when there are abnormal nodes in the
neighborhood due to this message passing method. In this paper, inspired by the
Neural Architecture Search method, we present an algorithm that recognizes
abnormal nodes and automatically excludes them from information aggregation.
Experiments on various real worlds datasets show that our proposed Neural
Architecture Search-based Anomaly Resistance Graph Neural Network (NASAR-GNN)
is actually effective.
