Signed graphs with positive and negative edges can model complex relationships in social networks. Leveraging on balance theory that deduces edge signs from multi-hop node pairs, signed graph learning can generate node embeddings that preserve both structural and sign information. However, training on sensitive signed graphs raises significant privacy concerns, as model parameters may leak private link information. Existing methods with differential privacy (DP) typically rely on edge or gradient perturbation for protecting unsigned graphs. Yet, they are not well-suited for signed graphs: edge perturbation may trigger cascading errors in edge sign inference under balance theory, while gradient perturbation necessitates substantial noise injection due to increased gradient sensitivity arising from node interdependence and gradient polarity change caused by sign flips. In this paper, motivated by the robustness of adversarial learning to noisy interactions, we present ASGL, a privacy-preserving adversarial signed graph learning method that preserves high utility while achieving node-level DP. We first decompose signed graphs into positive and negative subgraphs based on edge signs, and then design a gradient-perturbed adversarial module to approximate the true signed connectivity distribution. In particular, the gradient perturbation helps mitigate cascading errors, while the subgraph separation facilitates sensitivity reduction. Further, we devise a constrained breadth-first search tree strategy that fuses with balance theory to identify the edge signs between generated node pairs. This strategy also enables gradient decoupling, thereby effectively lowering gradient sensitivity. Extensive experiments on real-world datasets show that ASGL achieves favorable privacy-utility trade-offs across multiple downstream tasks.

Intelligent fault diagnosis of the complex systems has made great progress based on the availability of massive labeled data. However, due to the diversity of working conditions and the lack of sufficient fault samples in practice, the generalization of the existing fault diagnosis methods are weak. To handle this issue, a multichannel domain adaptation graph convolutional network method is proposed. In the proposed network, a feature mapping layer based on convolutional neural network is used first to extract features from input data, which then are transmitted to the graph generator to construct two association graphs. After that, three distributed graph convolutional networks are used to extract the specific and common embeddings from two association graphs and their combination. Meanwhile, to fuse these embeddings adaptively, an attention mechanism is used to learn importance weights. Besides, a domain discriminator is leveraged to reduce the distribution discrepancy of different data domains. Finally, a label classifier is used to output fault diagnosis results. Two experimental studies with different signal types show that the proposed method not only presents better diagnosis performance than existing methods with few samples, but also can extract domain-invariant features for cross-domain under varying working conditions.

In the conventional graph convolutional network (GCN)-based fault diagnosis method, multilayer GCN model is often used for feature extraction. However, the application of multilayer GCN will encounter oversmoothing problem, and thus reduce the diagnostic performance. Therefore, the oversmoothing relief GCN (OsR-GCN) method is proposed. Specifically, two association graph construction methods, namely the Euclidean distance (ED)-based method and the structure analysis (SA)-based method, are first introduced. Then, the constructed graph and measurements are input to the OsR-GCN model, in which a weight coefficient is proposed to relieve the oversmoothing problem. Next, an improved particle swarm optimization algorithm is introduced to find the optimal weight coefficient. Finally, the proposed method is applied to diagnose the pulse rectifier faults in a hardware-in-the-loop simulated traction control system of high-speed trains. The achieved results show that the proposed method outperforms the existing fault diagnosis methods.