mlsanityData to 7 Oct 2026

Spectral Subspace Clustering for Attributed Graphs

arXiv 2411.11074 ↗First posted 17 Nov 2024cs.SI cs.LGcited by 6

Not on the map. Why?

Abstract (arXiv)

Subspace clustering seeks to identify subspaces that segment a set of n data points into k (k<<n) groups, which has emerged as a powerful tool for analyzing data from various domains, especially images and videos. Recently, several studies have demonstrated the great potential of subspace clustering models for partitioning vertices in attributed graphs, referred to as SCAG. However, these works either demand significant computational overhead for constructing the nxn self-expressive matrix, or fail to incorporate graph topology and attribute data into the subspace clustering framework effectively, and thus, compromise result quality. Motivated by this, this paper presents two effective and efficient algorithms, S2CAG and M-S2CAG, for SCAG computation. Particularly, S2CAG obtains superb performance through three major contributions. First, we formulate a new objective function for SCAG with a refined representation model for vertices and two non-trivial constraints. On top of that, an efficient linear-time optimization solver is developed based on our theoretically grounded problem transformation and well-thought-out adaptive strategy. We then conduct an in-depth analysis to disclose the theoretical connection of S2CAG to conductance minimization, which further inspires the design of M-S2CAG that maximizes the modularity. Our extensive experiments, comparing S2CAG and M-S2CAG against 17 competitors over 8 benchmark datasets, exhibit that our solutions outperform all baselines in terms of clustering quality measured against the ground truth while delivering high efficiency

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 32 of its 105 references that are in the map. First posted on arXiv 17 Nov 2024.

Builds on

32 of 105 references.

References · 32

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20 more
  1. 2021Interpreting and Unifying Graph Neural Networks with An Optimization Frameworkcited by 131
  2. 2021Deep Graph Clustering via Dual Correlation Reductioncited by 97
  3. 2017Deep Subspace Clustering Networkscited by 94
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  8. 2022A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resourcecited by 26
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  12. 2015Clustering attributed graphs: models, measures and methodscited by 20
  13. 2020Large-Scale Subspace Clustering via k-Factorizationcited by 14
  14. 2021Towards Clustering-friendly Representations: Subspace Clustering via Graph Filteringcited by 14
  15. 2021CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional Network for Clusteringcited by 11
  16. 2023Efficient High-Quality Clustering for Large Bipartite Graphscited by 11
  17. 2021Effective and Scalable Clustering on Massive Attributed Graphscited by 10
  18. 2024Effective Clustering on Large Attributed Bipartite Graphscited by 7
  19. 2024A Versatile Framework for Attributed Network Clustering via K-Nearest Neighbor Augmentationcited by 6
  20. 2014Subspace clustering using a symmetric low-rank representationcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 3.

6 papers.

  1. 2025Cross-Contrastive Clustering for Multimodal Attributed Graphs with Dual Graph Filteringcited by 14
  2. 2024Leveraging Large Language Models for Effective Label-free Node Classification in Text-Attributed Graphscited by 6
  3. 2025Effective Clustering for Large Multi-Relational Graphscited by 2
  4. 2026CLUBench: A Clustering Benchmarkcited by 1
  5. 2026Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clusteringcited by 0
  6. 2026ComNetX: Local Hierarchical Adaptation for Dynamic Community Detectioncited by 0