一石二鸟：图数据无监督学习中的检测与分类协同

李思聪; 王飞; 魏子令; 陈曙晖

doi:10.12263/DZXB.20250761

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一石二鸟：图数据无监督学习中的检测与分类协同

学术论文 | 更新时间：2026-06-04

- 一石二鸟：图数据无监督学习中的检测与分类协同
- Joint Detection and Classification in Unsupervised Graph Learning
- 电子学报 2026年54卷第1期页码：153-166
- 作者机构：
  
  国防科技大学计算机学院，湖南长沙 410073
- 作者简介：
  
  [ "李思聪女，2000年8月出生于陕西省西安市。现为国防科技大学计算机学院博士研究生。主要研究方向为异常检测、开放环境下的机器学习。E-mail: lisicong@nudt.edu.cn" ]
  [ "王飞女，1985年3月出生于吉林省吉林市。现为国防科技大学副研究员。主要研究方向为机器学习、网络流量异常检测。E-mail: wangfei09a@nudt.edu.cn" ]
  [ "魏子令男，1991年11月出生于湖南省邵阳市。现为国防科技大学副研究员。主要研究方向为网络取证和网络优化。E-mail: weiziling@nudt.edu.cn" ]
  [ "陈曙晖男，1974年7月出生于湖南省长沙市。现为国防科技大学研究员。主要研究方向为网络取证和网络流量处理。E-mail: shchen@nudt.edu.cn" ]
- 基金信息：
- DOI：10.12263/DZXB.20250761
  中图分类号： TP181;
- 收稿：2025-09-04，
  
  录用：2025-12-29，
  
  纸质出版：2026-01-25
- 稿件说明：
移动端阅览
李思聪, 王飞, 魏子令, 等. 一石二鸟：图数据无监督学习中的检测与分类协同[J]. 电子学报, 2026, 54(01): 153-166.

LI Sicong, WANG Fei, WEI Ziling, et al. Joint Detection and Classification in Unsupervised Graph Learning[J]. Acta Electronica Sinica, 2026, 54(01): 153-166.
李思聪, 王飞, 魏子令, 等. 一石二鸟：图数据无监督学习中的检测与分类协同[J]. 电子学报, 2026, 54(01): 153-166. DOI：10.12263/DZXB.20250761

LI Sicong, WANG Fei, WEI Ziling, et al. Joint Detection and Classification in Unsupervised Graph Learning[J]. Acta Electronica Sinica, 2026, 54(01): 153-166. DOI：10.12263/DZXB.20250761

摘要

现实世界中的图机器学习系统通常运行于开放环境，测试阶段不可避免地接触到与训练分布不一致的样本，这违背了传统监督学习中训练与测试同分布的假设。模型不仅需要在分布内（In-Distribution，ID）样本上保持稳定的分类性能，还需具备识别并拒绝分布外（Out-Of-Distribution，OOD）数据的能力，以避免过度自信的错误预测。由于图数据中节点属性与拓扑结构高度耦合，分布偏移往往以隐式形式发生，使得图OOD检测较欧氏数据更加复杂。现有图OOD检测方法通常依赖强监督假设，如引入预标注的异常样本，或假设辅助OOD数据与ID数据在特征空间中显著可分。然而在实际应用中，OOD数据多以无标注、与ID数据天然混杂的形式出现，例如社交网络中的跨平台用户或推荐系统中的冷启动节点。这类野生数据难以通过先验规则进行显式区分，限制了现有方法在开放环境下的适用性。针对这一问题，本文提出一种全开放训练范式，在无需任何OOD标注或分布先验的条件下，利用无标注ID/OOD混合数据联合优化图节点分类与OOD检测任务。该方法通过构建带约束的优化目标，在严格约束ID分类误差与误检率的同时，引导模型提升对潜在OOD样本的识别能力，从而刻画真实开放环境中ID与OOD分布的隐式耦合关系。在方法层面，引入基于能量函数的检测机制，将图神经网络输出映射为能量值以度量样本与训练分布的一致性。能量约束引导模型在表示空间中形成可分离的分布结构，使ID样本集中于低能量区域，而潜在OOD样本对应较高能量，从而实现有效区分。该机制避免了基于Softmax置信度方法在分布外场景下的过度自信问题，并使检测目标能够直接作用于图表示学习过程。为求解上述带约束优化问题，本文采用增广拉格朗日方法，在训练过程中动态平衡约束满足与目标优化，增强模型在混合分布下的稳定性。实验结果显示，该方法在多个真实世界图数据集上均取得显著性能提升。在Twitch数据集上，AUROC和AUPR分别达到95.97%和92.84%，较当前最优基线GNNsafe++提升超过21个百分点，同时将误报率控制在12.30%，验证了其在无强监督条件下的有效性与鲁棒性。

Abstract

Real-world graph machine learning systems typically operate in open environments

where test-time data inevitably deviate from the training distribution

violating the common assumption of identical training and testing distributions in supervised learning. In this setting

models are required not only to maintain stable classification performance on in-distribution (ID) samples

but also to accurately identify and reject out-of-distribution (OOD) data to avoid overconfident erroneous predictions. Due to the strong coupling between node attributes and graph topology

distribution shifts in graph data often occur implicitly

making graph OOD detection more challenging than its Euclidean counterpart. Existing graph OOD detection methods commonly rely on strong supervision assumptions

such as the availability of pre-labeled anomalous samples or the assumption that auxiliary OOD data are clearly separable from ID data in the feature space. However

in practical applications

OOD samples typically appear in an unlabeled and naturally mixed manner with ID data

as observed in cross-platform users in social networks or cold-start nodes in recommendation systems. Such wild data are difficult to distinguish using prior rules

which limits the applicability of existing approaches in open environments. To address this issue

we propose a fully open training paradigm that jointly optimizes graph node classification and OOD detection using unlabeled ID/OOD mixed data

without requiring any OOD annotations or distributional priors. The proposed method formulates a constrained optimization objective that strictly controls ID classification error and false positive rates

while encouraging the model to improve its capability to identify potential OOD samples

thereby capturing the implicit coupling between ID and OOD distributions in real-world open settings. At the methodological level

we introduce an energy-based detection mechanism that maps the outputs of graph neural networks to energy values

which quantify the consistency of samples with the training distribution. The imposed energy constraints guide the model to learn separable representations

where ID samples concentrate in low-energy regions while potential OOD samples are pushed toward higher-energy regions. This design alleviates the overconfidence issue of Softmax-based confidence methods under distribution shifts and allows the detection objective to directly influence graph representation learning. To effectively solve the resulting constrained optimization problem

we adopt an augmented Lagrangian approach that dynamically balances constraint satisfaction and objective optimization during training

enhancing model stability under mixed distributions. Experimental results on multiple real-world graph datasets demonstrate significant performance improvements. On the Twitch dataset

the proposed method achieves an AUROC of 95.97% and an AUPR of 92.84%

outperforming the current state-of-the-art baseline GNNsafe++ by over 21 percentage points

while maintaining a false positive rate of 12.30%. These results confirm the effectiveness and robustness of the proposed framework under fully unsupervised and open-world conditions.

关键词

Keywords

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