Network inference using asynchronously updated kinetic Ising model

Hong-Li Zeng, Erik Aurell, Mikko Alava, and Hamed Mahmoudi
Phys. Rev. E 83, 041135 – Published 29 April 2011

Abstract

Network structures are reconstructed from dynamical data by respectively naive mean field (nMF) and Thouless-Anderson-Palmer (TAP) approximations. TAP approximation adds simple corrections to the nMF approximation, taking into account the effect of the focused spin on itself via its influence on other neighboring spins. For TAP approximation, we use two methods to reconstruct the network: (a) iterative method; (b) casting the inference formula to a set of cubic equations and solving it directly. We investigate inference of the asymmetric Sherrington-Kirkpatrick (aS-K) model using asynchronous update. The solutions of the set of cubic equations depend on temperature T in the aS-K model, and a critical temperature Tc2.1 is found. The two methods for TAP approximation produce the same results when the iterative method is convergent. Compared to nMF, TAP is somewhat better at low temperatures, but approaches the same performance as temperature increases. Both nMF and TAP approximation reconstruct better for longer data length L, but for the degree of improvement, TAP performs better than nMF.

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  • Received 29 November 2010

DOI:https://doi.org/10.1103/PhysRevE.83.041135

©2011 American Physical Society

Authors & Affiliations

Hong-Li Zeng1,*, Erik Aurell2,3, Mikko Alava1, and Hamed Mahmoudi3

  • 1Department of Applied Physics, Aalto University, FIN-00076 Aalto, Finland
  • 2Linnaeus Centre, KTH-Royal Institute of Technology, S-100 44 Stockholm, Sweden
  • 3Department of Information and Computer Science, Aalto University, FIN-00076 Aalto, Finland

  • *hongli.zeng@tkk.fi

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Vol. 83, Iss. 4 — April 2011

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