Page 406 - 《软件学报》2025年第5期

2306 软件学报 2025 年第 36 卷第 5 期

神经网络的节点分类任务. 首先, 针对数据不一致问题以及数据不平衡问题, 提出基于类别感知的恶意节点检测方
法 CAMD. CAMD 通过引入类别感知注意力系数、不一致图神经网络编码器以及类别感知不平衡损失函数, 增加
了不同类型节点表示的区分度, 保留了节点原本的信息以及表示学习过程中不同局域性的信息, 增加节点表示的
+
表达能力. 接下来, 针对 CAMD 在标签稀缺情况下效果受限的问题, 提出了基于图对比学习的方法 CAMD , 引入
数据增强、自监督图对比学习以及基于类别感知的平衡图对比学习, 使模型在标签稀缺情况下取得良好的效果.
+
在 5 个真实世界数据集上的大量实验表明, 本文提出的 CAMD 与 CAMD 方法的恶意节点检测性能由于其他基线
方法. 未来的工作包括进一步探索数据不一致场景下, 基于图的标签传播方法对模型的效果提升, 以及考虑采用类
似 GraphSAGE 的归纳式学习方法以减少计算内存占用, 从而使模型适用于更大规模的图数据.

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