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Explainability for Graph Spectral Analysis Google Scholar

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General Science
Computer Science

Volume7-Issue5
Dates: Received: 2026-05-20 | Accepted: 2026-05-25 | Published: 2026-05-26
Pages: 1-3

Abstract

FullText HTML FullText PDF DOI: 10.37871/jbres2301


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Copyright

© 2026 Klopotek MA, et al. Distributed under Creative Commons CC-BY 4.0

How to cite this article

Klopotek MA, Wierzchon ST, Starosta B, Borkowski P, Czerski D. Explainability for Graph Spectral Analysis. J Biomed Res Environ Sci. 2026 May 26; 7(5): 3. Doi: 10.37872/jbres2301


Subject area(s)

Computer Science

References


  1. Wierzcho? ST, K?opotek MA. Modern Algorithms of Cluster Analysis. Studies in Big Data. Vol. 34. Springer Verlag; 2018.
  2. Cao H. Recent advances in text embedding: A comprehensive review of top-performing methods on the MTEB benchmark. CoRR. 2024;abs/2406.01607v1. doi:10.48550/arXiv.2406.01607
  3. Starosta B, K?opotek MA, Wierzcho? ST, Czerski D, Sydow M, Borkowski P. Explainable graph spectral clustering of text documents. PLoS One. 2025;20(2):e0313238. doi:10.1371/journal.pone.0313238
  4. Borg I, Groenen PJF. Modern Multidimensional Scaling: Theory and Applications. 2nd ed. Springer Series in Statistics. New York (NY): Springer; 2005. doi:10.1007/0-387-28981-X
  5. K?opotek MA, Wierzcho? ST, Starosta B, Borkowski P, Czerski D, Laskowski E. Explainable graph spectral clustering for glove-like text embeddings. 2025.
  6. K?opotek MA, Wierzcho? ST, Starosta B, Borkowski P, Czerski D. Towards explainable graph spectral clustering for BERT embeddings. J Autom Mob Robot Intell Syst. 2026;20(1):53-65.
  7. K?opotek MA, Wierzcho? ST, Starosta B, Czerski D, Borkowski P. A method for handling negative similarities in explainable graph spectral clustering of text documents – extended version. 2025.

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