Network Deconvolution - A General Method to Distinguish Direct Dependencies over Networks
نویسندگان
چکیده
1 Analyzing General Properties of Network Deconvolution 3 1.1 Network deconvolution algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Optimality analysis of network deconvolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Modeling assumptions and intuitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Decomposable and non-decomposable matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4.1 Asymmetric decomposable matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4.2 General non-decomposable matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.5 Robustness against noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.6 Scaling effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.7 Computational complexity analysis of network deconvolution . . . . . . . . . . . . . . . . . . . . . . . 7 1.7.1 Using sparsity in eigen decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.7.2 Parallelization of network deconvolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
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