  Dynamic graphs are extensively employed for detecting anomalous behavior in
nodes within the Internet of Things (IoT). 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. This paper presents a novel graph generation model, called
CGGM, designed specifically to generate a larger number of nodes belonging to
the minority class. The mechanism for generating an adjacency matrix, through
adaptive sparsity, enhances flexibility in its structure. The feature
generation module, called multidimensional features generator (MFG) to generate
node features along with topological information. Labels are transformed into
embedding vectors, serving as conditional constraints to control the generation
of synthetic data across multiple categories. Using a multi-stage loss, the
distribution of synthetic data is adjusted to closely resemble that of real
data. In extensive experiments, we show that CGGM's synthetic data outperforms
state-of-the-art methods across various metrics. Our results demonstrate
efficient generation of diverse data categories, robustly enhancing
multi-category classification model performance.
