The rapid expansion of Internet of Things (IoT) ecosystems has led to increasingly complex and heterogeneous network topologies. Traditional network monitoring and visualization tools rely on aggregated metrics or static representations, which fail to capture the evolving relationships and structural dependencies between devices. Although Graph Neural Networks (GNNs) offer a powerful way to learn from relational data, their internal representations often remain opaque and difficult to interpret for security-critical operations. Consequently, this work introduces an interpretable pipeline that generates directly visualizable low-dimensional representations by mapping high-dimensional embeddings onto a latent manifold. This projection enables the interpretable monitoring and interoperability of evolving network states, while integrated feature attribution techniques decode the specific characteristics shaping the manifold structure. The framework achieves a classification F1-score of 0.830 for intrusion detection while also highlighting phenomena such as concept drift. Ultimately, the presented approach bridges the gap between high-dimensional GNN embeddings and human-understandable network behavior, offering new insights for network administrators and security analysts.

翻译：物联网（IoT）生态系统的快速扩张导致了日益复杂和异构的网络拓扑结构。传统的网络监控与可视化工具依赖于聚合指标或静态表示，这些方法无法捕捉设备间不断演变的关系和结构依赖性。尽管图神经网络（GNN）为从关系数据中学习提供了强大手段，但其内部表示对于安全关键操作往往仍不透明且难以解释。因此，本研究提出了一种可解释的流程，通过将高维嵌入映射到潜在流形上，生成可直接可视化的低维表示。该投影实现了对演化网络状态的可解释监控与互操作性，同时集成的特征归因技术解码了塑造流形结构的具体特征。该框架在入侵检测中实现了0.830的分类F1分数，同时还能突出概念漂移等现象。最终，所提出的方法弥合了高维GNN嵌入与人类可理解的网络行为之间的鸿沟，为网络管理员和安全分析师提供了新的见解。