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New algorithms tackle complex matrix factorization for network analysis

By PulseAugur Editorial · [2 sources] ·

Researchers have developed novel heuristic algorithms to tackle the complex symmetric multi-type orthogonal non-negative matrix tri-factorization problem. These methods, including a fixed-point approach and an ADAM-based technique, aim to find high-quality local solutions for this non-convex optimization challenge. Evaluations on synthetic data and citation networks demonstrate the algorithms' effectiveness in recovering factorizations and producing competitive embeddings for tasks like link prediction and node classification. AI

IMPACT Introduces new methods for embedding generation, potentially improving downstream AI tasks in network analysis and clustering.

RANK_REASON The cluster contains a research paper detailing new algorithms for a specific mathematical problem with applications in network analysis.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New algorithms tackle complex matrix factorization for network analysis

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The cluster contains a research paper detailing new algorithms for a specific mathematical problem with applications in network analysis.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Rok Hribar, Gregor Papa, Janez Povh, Andrej Kastrin ·

    On solving symmetric multi-type orthogonal non-negative matrix tri-factorization problem

    arXiv:2606.08291v1 Announce Type: new Abstract: We study the symmetric multi-type orthogonal non-negative matrix tri-factorization problem, where several symmetric non-negative matrices are simultaneously approximated by factors of the form $GS_{i}G^{\top}$, with a shared non-neg…

  2. arXiv cs.LG TIER_1 English(EN) · Andrej Kastrin ·

    On solving symmetric multi-type orthogonal non-negative matrix tri-factorization problem

    We study the symmetric multi-type orthogonal non-negative matrix tri-factorization problem, where several symmetric non-negative matrices are simultaneously approximated by factors of the form $GS_{i}G^{\top}$, with a shared non-negative and orthogonal factor $G$. This model is m…

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