  Machine learning has been successfully applied to complex network analysis in
various areas, and graph neural networks (GNNs) based methods outperform
others. Recently, adversarial attack on networks has attracted special
attention since carefully crafted adversarial networks with slight
perturbations on clean network may invalid lots of network applications, such
as node classification, link prediction, and community detection etc. Such
attacks are easily constructed with serious security threat to various analyze
methods, including traditional methods and deep models. To the best of our
knowledge, it is the first time that defense method against network adversarial
attack is discussed. In this paper, we are interested in the possibility of
defense against adversarial attack on network, and propose defense strategies
for GNNs against attacks. First, we propose novel adversarial training
strategies to improve GNNs' defensibility against attacks. Then, we
analytically investigate the robustness properties for GNNs granted by the use
of smooth defense, and propose two special smooth defense strategies: smoothing
distillation and smoothing cross-entropy loss function. Both of them are
capable of smoothing gradient of GNNs, and consequently reduce the amplitude of
adversarial gradients, which benefits gradient masking from attackers. The
comprehensive experiments show that our proposed strategies have great
defensibility against different adversarial attacks on four real-world networks
in different network analyze tasks.
