Papers › Efficient graph convolution for joint node representation learning and clustering
Efficient graph convolution for joint node representation learning and clustering
Chakib Fettal, Lazhar Labiod, Mohamed Nadif
Attributed graphs are used to model a wide variety of real-world networks. Recent graph convolutional network-based representation learning methods have set state-of-the-art results on the clustering of attributed graphs. However, these approaches deal with clustering as a downstream task while better performances can be attained by incorporating the clustering objective into the representation learning process. In this paper, we propose, in a unified framework, an objective function taking into account both tasks simultaneously. Based on a variant of the simple graph convolutional network, our model does clustering by minimizing the difference between the convolved node representations and their reconstructed cluster representatives. We showcase the efficiency of the derived algorithm against state-of-the-art methods both in terms of clustering performance and computational cost on thede facto benchmark graph clustering datasets. We further demonstrate the usefulness of the proposed approach for graph visualization through generating embeddings that exhibit a clustering structure.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections