mlsanityData to 13 Oct 2026

Simplifying Clustering with Graph Neural Networks

arXiv 2207.08779 ↗First posted 18 Jul 2022cs.LGcited by 8

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Abstract (arXiv)

The objective functions used in spectral clustering are usually composed of two terms: i) a term that minimizes the local quadratic variation of the cluster assignments on the graph and; ii) a term that balances the clustering partition and helps avoiding degenerate solutions. This paper shows that a graph neural network, equipped with suitable message passing layers, can generate good cluster assignments by optimizing only a balancing term. Results on attributed graph datasets show the effectiveness of the proposed approach in terms of clustering performance and computation time.

Key facts. Cited by 8 papers in the map (data through 13 Oct 2026). Builds on 12 of its 16 references that are in the map. First posted on arXiv 18 Jul 2022.

Builds on

12 of 16 references.

References · 12

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,318
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,471
  3. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,086
  4. 2016node2vec: Scalable Feature Learning for Networkscited by 1,915
  5. 2014DeepWalk: Online Learning of Social Representationscited by 1,544
  6. 2016Variational Graph Auto-Encoderscited by 1,379
  7. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 787
  8. 2017Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2veccited by 198
  9. 2020Graph Clustering with Graph Neural Networkscited by 163
  10. 2022A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directionscited by 73
  11. 2021Understanding Pooling in Graph Neural Networkscited by 69
  12. 2019Deep Divergence-Based Approach to Clusteringcited by 13

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 3; 2026: 1.

8 papers.

  1. 2024A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizabilitycited by 7
  2. 2024MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networkscited by 5
  3. 2025BN-Pool: Bayesian Nonparametric Pooling for Graphscited by 2
  4. 2025Torch Geometric Pool: the PyTorch library for pooling in Graph Neural Networkscited by 2
  5. 2023BHGNN-RT: Network embedding for directed heterogeneous graphscited by 1
  6. 2024MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Lengthcited by 1
  7. 2026The Role of Node Features in Graph Poolingcited by 1
  8. 2025GraphFLEx: Structure Learning Framework for Large Expanding Graphscited by 0