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        "author": "Ankit N. Khambhati",
        "author_email": "akhambhati@gmail.com",
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Nokia Open Source License",
            "Natural Language :: English",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.6",
            "Topic :: Software Development :: Build Tools"
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        "description": "Functional Subgraph\n====================\n\nA machine learning toolbox for the analysis of dynamic graphs.\n\n*Functional Subgraph* implements non-negative matrix factorization to decompose\ntime-varying, dynamic graphs into a composite set of parts-based, additive\nsubgraphs.\n\n\nQuick-Start\n-----------\nNon-Negative Matrix Factorization for dynamic graphs, such that:\n\n    A ~= WH\n    Constraints:\n        A, W, H >= 0\n        L2-Regularization on W\n        L1-Sparsity on H\n\nImplementation is based on :\n\n    1. Jingu Kim, Yunlong He, and Haesun Park. Algorithms for Nonnegative\n            Matrix and Tensor Factorizations: A Unified View Based on Block\n            Coordinate Descent Framework.\n            Journal of Global Optimization, 58(2), pp. 285-319, 2014.\n\n    2. Jingu Kim and Haesun Park. Fast Nonnegative Matrix Factorization:\n            An Active-set-like Method And Comparisons.\n            SIAM Journal on Scientific Computing (SISC), 33(6),\n            pp. 3261-3281, 2011.\n\nModified from: https://github.com/kimjingu/nonnegfac-python\n\n\n",
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        "name": "FunctionalSubgraph",
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