Clustering using graph convolution networks - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Progress in Artificial Intelligence Année : 2024

Clustering using graph convolution networks

Résumé

Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction. However, important unsupervised problems on graphs, such as graph clustering, have proved more resistant to advances in GNNs. Graph clustering has the same overall goal as node pooling in GNNs-does this mean that GNN pooling methods do a good job at clustering graphs? Surprisingly, the answer is no-current GNN pooling methods often fail to recover the cluster structure in cases where simple baselines, such as k-means applied on learned representations, work well. We investigate further by carefully designing a set of experiments to study different signal-to-noise scenarios both in graph structure and attribute data. To address these methods' poor performance in clustering, we introduce Deep Modularity Networks (DMoN), an unsupervised pooling method inspired by the modularity measure of clustering quality, and show how it tackles recovery of the challenging clustering structure of real-world graphs. Similarly, on real-world data, we show that DMoN produces high quality clusters which correlate strongly with ground truth labels, achieving state-of-the-art results with over 40% improvement over other pooling methods across different metrics.
Fichier principal
Vignette du fichier
Progress_In_Artificial_Intelligence (1).pdf (1 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04460666 , version 1 (15-02-2024)

Identifiants

Citer

Maria Al Jreidy, Joseph Constantin, Fadi Dornaika, Denis Hamad. Clustering using graph convolution networks. Progress in Artificial Intelligence, 2024, ⟨10.1007/s13748-023-00310-z⟩. ⟨hal-04460666⟩
7 Consultations
18 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More