Introduces an iterative self-learning GNN method for clustering attributed graphs that combines network and attribute information across multiple rounds and outperforms single-round training on synthetic data.
arXiv:1909.12201v1 [cs.LG] (2018)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
The paper integrates GNN community detection into a PUM-GBF interpolation scheme and reports accurate signal reconstructions on geometric and urban graph benchmarks.
citing papers explorer
-
Clustering Node Attributed Networks with Graph Neural Networks and Self Learning
Introduces an iterative self-learning GNN method for clustering attributed graphs that combines network and attribute information across multiple rounds and outperforms single-round training on synthetic data.
-
Graph Neural Networks for Community Detection in Graph Signal Analysis
The paper integrates GNN community detection into a PUM-GBF interpolation scheme and reports accurate signal reconstructions on geometric and urban graph benchmarks.