In this paper, we propose a machine learning process for clustering large-scale social Internet-of-things (SIoT) devices into several groups of related devices sharing strong relations. To this end, we generate undirected weighted graphs based on the historical dataset of IoT devices and their social relations. Using the adjacency matrices of these graphs and the IoT devices' features, we embed the graphs' nodes using a Graph Neural Network (GNN) to obtain numerical vector representations of the IoT devices. The vector representation does not only reflect the characteristics of the device but also its relations with its peers. The obtained node embeddings are then fed to a conventional unsupervised learning algorithm to determine the clusters accordingly. We showcase the obtained IoT groups using two well-known clustering algorithms, specifically the K-means and the density-based algorithm for discovering clusters (DBSCAN). Finally, we compare the performances of the proposed GNN-based clustering approach in terms of coverage and modularity to those of the deterministic Louvain community detection algorithm applied solely on the graphs created from the different relations. It is shown that the framework achieves promising preliminary results in clustering large-scale IoT systems.

中文翻译：

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基于图神经网络的社交物联网聚类

在本文中，我们提出了一种机器学习过程，用于将大型社交物联网 (SIoT) 设备聚类为几组共享强关系的相关设备。为此，我们根据物联网设备的历史数据集及其社会关系生成无向加权图。使用这些图的邻接矩阵和 IoT 设备的特征，我们使用图神经网络 (GNN) 嵌入图的节点以获得 IoT 设备的数值向量表示。向量表示不仅反映了设备的特性，还反映了它与其对等方的关系。然后将获得的节点嵌入馈送到传统的无监督学习算法以相应地确定集群。我们使用两种著名的聚类算法展示了获得的物联网组，特别是 K-means 和基于密度的聚类发现算法 (DBSCAN)。最后，我们将所提出的基于 GNN 的聚类方法在覆盖率和模块化方面的性能与仅应用于从不同关系创建的图的确定性 Louvain 社区检测算法的性能进行了比较。结果表明，该框架在集群大规模物联网系统方面取得了有希望的初步结果。