Signed graph neural networks (SGNNs) has recently drawn more attention as
many real-world networks are signed networks containing two types of edges:
positive and negative. The existence of negative edges affects the SGNN
robustness on two aspects. One is the semantic imbalance as the negative edges
are usually hard to obtain though they can provide potentially useful
information. The other is the structural unbalance, e.g. unbalanced triangles,
an indication of incompatible relationship among nodes. In this paper, we
propose a balancing augmentation method to address the above two aspects for
SGNNs. Firstly, the utility of each negative edge is measured by calculating
its occurrence in unbalanced structures. Secondly, the original signed graph is
selectively augmented with the use of (1) an edge perturbation regulator to
balance the number of positive and negative edges and to determine the ratio of
perturbed edges to original edges and (2) an edge utility filter to remove the
negative edges with low utility to make the graph structure more balanced.
Finally, a SGNN is trained on the augmented graph which effectively explores
the credible relationships. A detailed theoretical analysis is also conducted
to prove the effectiveness of each module. Experiments on five real-world
datasets in link prediction demonstrate that our method has the advantages of
effectiveness and generalization and can significantly improve the performance
of SGNN backbones.

提出了一种平衡增强方法，用于解决具有两类边（正向和负向）的有符号网络中存在的负边对有符号图神经网络的鲁棒性可能产生的语义和结构不平衡问题。