  Graph Neural Networks (GNNs) have attracted increasing attention due to its
successful applications on various graph-structure data. However, recent
studies have shown that adversarial attacks are threatening the functionality
of GNNs. Although numerous works have been proposed to defend adversarial
attacks from various perspectives, most of them can be robust against the
attacks only on specific scenarios. To address this shortage of robust
generalization, we propose to defend the adversarial attacks on GNN through
applying the Spatio-Temporal sparsification (called ST-Sparse) on the GNN
hidden node representation. ST-Sparse is similar to the Dropout regularization
in spirit. Through intensive experiment evaluation with GCN as the target GNN
model, we identify the benefits of ST-Sparse as follows: (1) ST-Sparse shows
the defense performance improvement in most cases, as it can effectively
increase the robust accuracy by up to 6\% improvement; (2) ST-Sparse
illustrates its robust generalization capability by integrating with the
existing defense methods, similar to the integration of Dropout into various
deep learning models as a standard regularization technique; (3) ST-Sparse also
shows its ordinary generalization capability on clean datasets, in that
ST-SparseGCN (the integration of ST-Sparse and the original GCN) even
outperform the original GCN, while the other three representative defense
methods are inferior to the original GCN.
