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…eigenvalues and corresponding eigenvectors of a graph based on its adjacency list. - Utilized `lanczos_iteration` to construct tridiagonal matrices, optimized for large, sparse matrices. - Added `multiply_matrix_vector` for efficient matrix-vector multiplication using adjacency lists. - Included `validate_adjacency_list` for input validation. - Supports varied graph analysis applications, particularly for analyzing graph centrality. - Included type hints, comprehensive docstrings, and doctests. - PEP-8 compliant, with optimized handling of inputs and outputs. This module provides essential tools for eigenvalue-based graph analysis, ideal for centrality insights and structural assessments.
imSanko
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Looks Fine wait for [at] clauss
cclauss
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Dec 30, 2024
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Fixes: #12386
Title: Add Lanczos Eigenvector Algorithm for Graph-based Eigenvalue Computation
Description:
This pull request introduces a new algorithm for approximating the largest eigenvalues and eigenvectors of a symmetric matrix in the context of graphs, using the Lanczos method. The implementation leverages adjacency list representation to handle sparse graphs effectively and includes the following:
Commit:
Initial Commit: Add Lanczos Eigenvector Algorithm for Graph-based Eigenvalue Computation
find_lanczos_eigenvectorsfor efficient computation of k-largest eigenvalues and eigenvectors in sparse graphs.lanczos_iterationfor constructing tridiagonal matrices.multiply_matrix_vectorfor efficient adjacency-list matrix-vector multiplication.rufflinting.Checklist: