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Concentration of Multilinear Functions of the Ising Model with Applications to Network Data

2017-10-11NeurIPS 2017Code Available0· sign in to hype

Constantinos Daskalakis, Nishanth Dikkala, Gautam Kamath

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Abstract

We prove near-tight concentration of measure for polynomial functions of the Ising model under high temperature. For any degree d, we show that a degree-d polynomial of a n-spin Ising model exhibits exponential tails that scale as (-r^2/d) at radius r=_d(n^d/2). Our concentration radius is optimal up to logarithmic factors for constant d, improving known results by polynomial factors in the number of spins. We demonstrate the efficacy of polynomial functions as statistics for testing the strength of interactions in social networks in both synthetic and real world data.

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