Adversarial Signed Graph Learning with Differential Privacy

• URL: http://arxiv.org/abs/2512.00307v1
• Date: Sat, 29 Nov 2025 04:02:48 GMT
• Title: Adversarial Signed Graph Learning with Differential Privacy
• Authors: Haobin Ke, Sen Zhang, Qingqing Ye, Xun Ran, Haibo Hu,
• Abstract summary: Signed graphs with positive and negative edges can model complex relationships in social networks.<n>Training on sensitive signed graphs raises significant privacy concerns.<n>We present ASGL, a privacy-preserving adversarial signed graph learning method.
• Score: 13.459205297125534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 protection methods with differential privacy (DP) typically rely on edge or gradient perturbation for unsigned graph protection. Yet, they are not well-suited for signed graphs, mainly because edge perturbation tends to cascading errors in edge sign inference under balance theory, while gradient perturbation increases sensitivity due to node interdependence and gradient polarity change caused by sign flips, resulting in larger noise injection. 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.

Related papers

• Mitigating Over-Squashing in Graph Neural Networks by Spectrum-Preserving Sparsification [81.06278257153835]
  We propose a graph rewiring method that balances structural bottleneck reduction and graph property preservation.<n>Our method generates graphs with enhanced connectivity while maintaining sparsity and largely preserving the original graph spectrum.
  arXiv  Detail & Related papers  (2025-06-19T08:01:00Z)
• A Signed Graph Approach to Understanding and Mitigating Oversmoothing in GNNs [54.62268052283014]
  We present a unified theoretical perspective based on the framework of signed graphs.<n>We show that many existing strategies implicitly introduce negative edges that alter message-passing to resist oversmoothing.<n>We propose Structural Balanced Propagation (SBP), a plug-and-play method that assigns signed edges based on either labels or feature similarity.
  arXiv  Detail & Related papers  (2025-02-17T03:25:36Z)
• Effective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs [3.996726993941017]
  We propose ELISE, an effective and lightweight GNN-based approach for learning signed bipartite graphs.<n>We first extend personalized propagation to a signed bipartite graph, incorporating signed edges during message passing.<n>We then jointly learn node embeddings on a low-rank approximation of the signed bipartite graph, which reduces potential noise.
  arXiv  Detail & Related papers  (2024-12-25T00:39:38Z)
• Smoothed Graph Contrastive Learning via Seamless Proximity Integration [30.247207861739245]
  Graph contrastive learning (GCL) aligns node representations by classifying node pairs into positives and negatives. We present a Smoothed Graph Contrastive Learning model (SGCL) that injects proximity information associated with positive/negative pairs in the contrastive loss. The proposed SGCL adjusts the penalties associated with node pairs in contrastive loss by incorporating three distinct smoothing techniques.
  arXiv  Detail & Related papers  (2024-02-23T11:32:46Z)
• BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection [50.26074811655596]
  We propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE) By swapping the context embeddings between nodes and edges, we enable the mutual detection of node and edge anomalies. BOURNE can eliminate the need for negative sampling, thereby enhancing its efficiency in handling large graphs.
  arXiv  Detail & Related papers  (2023-07-28T00:44:57Z)
• Pseudo Contrastive Learning for Graph-based Semi-supervised Learning [67.37572762925836]
  Pseudo Labeling is a technique used to improve the performance of Graph Neural Networks (GNNs) We propose a general framework for GNNs, termed Pseudo Contrastive Learning (PCL)
  arXiv  Detail & Related papers  (2023-02-19T10:34:08Z)
• Are Gradients on Graph Structure Reliable in Gray-box Attacks? [56.346504691615934]
  Previous gray-box attackers employ gradients from the surrogate model to locate the vulnerable edges to perturb the graph structure. In this paper, we discuss and analyze the errors caused by the unreliability of the structural gradients. We propose a novel attack model with methods to reduce the errors inside the structural gradients.
  arXiv  Detail & Related papers  (2022-08-07T06:43:32Z)
• Contrastive Laplacian Eigenmaps [37.5297239772525]
  Graph contrastive learning attracts/disperses node representations for similar/dissimilar node pairs under some notion of similarity. We extend the celebrated Laplacian Eigenmaps with contrastive learning, and call them COntrastive Laplacian EigenmapS (COLES)
  arXiv  Detail & Related papers  (2022-01-14T14:59:05Z)
• GraphMI: Extracting Private Graph Data from Graph Neural Networks [59.05178231559796]
  We present textbfGraph textbfModel textbfInversion attack (GraphMI), which aims to extract private graph data of the training graph by inverting GNN. Specifically, we propose a projected gradient module to tackle the discreteness of graph edges while preserving the sparsity and smoothness of graph features. We design a graph auto-encoder module to efficiently exploit graph topology, node attributes, and target model parameters for edge inference.
  arXiv  Detail & Related papers  (2021-06-05T07:07:52Z)
• Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph [54.03786611989613]
  signed link prediction in social networks aims to reveal the underlying relationships (i.e. links) among users (i.e. nodes) We develop a unified framework, termed as Signed Infomax Hyperbolic Graph (textbfSIHG) In order to model high-order user relations and complex hierarchies, the node embeddings are projected and measured in a hyperbolic space with a lower distortion.
  arXiv  Detail & Related papers  (2020-11-25T05:09:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.