Anonymized GCN: A Novel Robust Graph Embedding Method via Hiding Node Position in Noise

by   Ao Liu, et al.

Graph convolution network (GCN) have achieved state-of-the-art performance in the task of node prediction in the graph structure. However, with the gradual various of graph attack methods, there are lack of research on the robustness of GCN. At this paper, we will design a robust GCN method for node prediction tasks. Considering the graph structure contains two types of information: node information and connection information, and attackers usually modify the connection information to complete the interference with the prediction results of the node, we first proposed a method to hide the connection information in the generator, named Anonymized GCN (AN-GCN). By hiding the connection information in the graph structure in the generator through adversarial training, the accurate node prediction can be completed only by the node number rather than its specific position in the graph. Specifically, we first demonstrated the key to determine the embedding of a specific node: the row corresponding to the node of the eigenmatrix of the Laplace matrix, by target it as the output of the generator, we designed a method to hide the node number in the noise. Take the corresponding noise as input, we will obtain the connection structure of the node instead of directly obtaining. Then the encoder and decoder are spliced both in discriminator, so that after adversarial training, the generator and discriminator can cooperate to complete the encoding and decoding of the graph, then complete the node prediction. Finally, All node positions can generated by noise at the same time, that is to say, the generator will hides all the connection information of the graph structure. The evaluation shows that we only need to obtain the initial features and node numbers of the nodes to complete the node prediction, and the accuracy did not decrease, but increased by 0.0293.


page 1

page 2

page 3

page 4


Learning Adaptive Neighborhoods for Graph Neural Networks

Graph convolutional networks (GCNs) enable end-to-end learning on graph ...

Cross-network transferable neural models for WLAN interference estimation

Airtime interference is a key performance indicator for WLANs, measuring...

Interpreting and Understanding Graph Convolutional Neural Network using Gradient-based Attribution Methods

In order to solve the problem that convolutional neural networks (CNN) a...

Node Feature Kernels Increase Graph Convolutional Network Robustness

The robustness of the much-used Graph Convolutional Networks (GCNs) to p...

Semi-Supervised Graph Embedding for Multi-Label Graph Node Classification

The graph convolution network (GCN) is a widely-used facility to realize...

Text Enriched Sparse Hyperbolic Graph Convolutional Networks

Heterogeneous networks, which connect informative nodes containing text ...

Joint embedding of structure and features via graph convolutional networks

The creation of social ties is largely determined by the entangled effec...

Please sign up or login with your details

Forgot password? Click here to reset