Dynamic graphs are extensively employed for detecting anomalous behavior in nodes within the Internet of Things (IoT). Graph generative models are often used to address the issue of imbalanced node categories in dynamic graphs. Nevertheless, the constraints it faces include the monotonicity of adjacency relationships, the difficulty in constructing multi-dimensional features for nodes, and the lack of a method for end-to-end generation of multiple categories of nodes. In this paper, we propose a novel graph generation model, called CGGM, specifically for generating samples belonging to the minority class. The framework consists two core module: a conditional graph generation module and a graph-based anomaly detection module. The generative module adapts to the sparsity of the matrix by downsampling a noise adjacency matrix, and incorporates a multi-dimensional feature encoder based on multi-head self-attention to capture latent dependencies among features. Additionally, a latent space constraint is combined with the distribution distance to approximate the latent distribution of real data. The graph-based anomaly detection module utilizes the generated balanced dataset to predict the node behaviors. Extensive experiments have shown that CGGM outperforms the state-of-the-art methods in terms of accuracy and divergence. The results also demonstrate CGGM can generated diverse data categories, that enhancing the performance of multi-category classification task.

翻译：动态图被广泛应用于物联网（IoT）中节点异常行为的检测。图生成模型常被用来解决动态图中节点类别不平衡的问题。然而，现有方法面临的限制包括邻接关系的单调性、难以构建节点的多维特征，以及缺乏一种端到端生成多类别节点的方法。本文提出了一种新颖的图生成模型，称为CGGM，专门用于生成属于少数类别的样本。该框架包含两个核心模块：一个条件图生成模块和一个基于图的异常检测模块。生成模块通过对噪声邻接矩阵进行下采样来适应矩阵的稀疏性，并融合了一个基于多头自注意力的多维特征编码器，以捕获特征间的潜在依赖关系。此外，模型将潜在空间约束与分布距离相结合，以逼近真实数据的潜在分布。基于图的异常检测模块利用生成的平衡数据集来预测节点行为。大量实验表明，CGGM在准确性和散度方面优于现有最先进的方法。结果还证明CGGM能够生成多样化的数据类别，从而提升了多类别分类任务的性能。