  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.
