基于图神经网络的社会物联网聚类
Graph Neural Networks-based Clustering for Social Internet of Things
摘要：
在本文中，我们提出了一个机器学习过程，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及其社会关系的历史数据集生成无向加权图。利用这些图的邻接矩阵和物联网设备的特征，我们使用图神经网络（GNN）嵌入图的节点，以获得物联网设备的数字矢量表示。矢量表示不仅反映了设备的特性，而且还反映了它与对等设备的关系。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聚类。我们展示了使用两种著名的聚类算法（特别是K-means和基于密度的集群发现算法（DBSCAN））获得的物联网组。最后，我们比较了所提出的基于GNN的聚类方法在覆盖率和模块化方面的性能的确定性鲁汶社区发现算法只适用于从不同的关系创建的图形。结果表明，该框架在聚类大规模物联网系统方面取得了可喜的初步成果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
