Abstract
It is an important and unsettled issue to distinguish direct dependencies from the indirect ones without any prior knowledge in biological networks and social networks, which contain important biological features and co-authorship information. We present a new algorithm, called balanced network deconvolution (BND), by exploiting eigen-decomposition and the statistical behavior of the eigenvalues of random symmetric matrices. Specially, the BND is a parameter-free algorithm that can be directly applied to different networks. Experimental results establish BND as a robust and general approach for filtering the transitive noise on various input matrices generated by different prediction algorithms.
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Sun, HP., Shen, HB. (2014). A Global Eigenvalue-Driven Balanced Deconvolution Approach for Network Direct-Coupling Analysis. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_43
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DOI: https://doi.org/10.1007/978-3-662-45643-9_43
Publisher Name: Springer, Berlin, Heidelberg
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