Network embedding maps a network into a low-dimensional Euclidean space, and
thus facilitate many network analysis tasks, such as node classification, link
prediction and community detection etc, by utilizing machine learning methods.
In social networks, we may pay special attention to user privacy, and would
like to prevent some target nodes from being identified by such network
analysis methods in certain cases. Inspired by successful adversarial attack on
deep learning models, we propose a framework to generate adversarial networks
based on the gradient information in Graph Convolutional Network (GCN). In
particular, we extract the gradient of pairwise nodes based on the adversarial
network, and select the pair of nodes with maximum absolute gradient to realize
the Fast Gradient Attack (FGA) and update the adversarial network. This process
is implemented iteratively and terminated until certain condition is satisfied,
i.e., the number of modified links reaches certain predefined value.
Comprehensive attacks, including unlimited attack, direct attack and indirect
attack, are performed on six well-known network embedding methods. The
experiments on real-world networks suggest that our proposed FGA behaves better
than some baseline methods, i.e., the network embedding can be easily disturbed
using FGA by only rewiring few links, achieving state-of-the-art attack
performance.

本研究基于图卷积网络的渐变信息生成对抗性网络，在社交网络等情境中，通过只重新链接少量节点即可干扰网络嵌入，保护目标节点的隐私。