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This paper investigates the problem of Graph Spectral Clustering (GSC) with negative similarities, resulting from document embeddings different from the traditional Term Vector Space (like doc2vec, GloVe, etc.). Solutions for combinatorial Laplacians and normalized Laplacians are discussed. An experimental investigation shows the advantages and disadvantages of solutions proposed in the literature and in this research. The research demonstrates that GloVe embeddings frequently cause failures of normalized Laplacian based GSC due to negative similarities. Application of methods curing similarity negativity leads to accuracy improvement for both combinatorial and normalized Laplacian based GSC. It also leads to applicability for GloVe embeddings of explanation methods developed for Term Vector Space embeddings.

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A Method for Handling Negative Similarities in Explainable Graph Spectral Clustering of Text Documents

  • 会议论文

  • 发表日期：

    2025年

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  • Mieczysław A. Kłopotek,
  • Sławomir T. Wierzchoń,
  • Bartłomiej Starosta,
  • Dariusz Czerski,
  • Piotr Borkowski

摘要

This paper investigates the problem of Graph Spectral Clustering (GSC) with negative similarities, resulting from document embeddings different from the traditional Term Vector Space (like doc2vec, GloVe, etc.). Solutions for combinatorial Laplacians and normalized Laplacians are discussed. An experimental investigation shows the advantages and disadvantages of solutions proposed in the literature and in this research. The research demonstrates that GloVe embeddings frequently cause failures of normalized Laplacian based GSC due to negative similarities. Application of methods curing similarity negativity leads to accuracy improvement for both combinatorial and normalized Laplacian based GSC. It also leads to applicability for GloVe embeddings of explanation methods developed for Term Vector Space embeddings.