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INFERRING THE DEPENDENCE GRAPH DENSITY OF BINARY GRAPHICAL MODELS IN HIGH DIMENSION
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DOI：10.1214/25-AOS2592.png)
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Abstract
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We consider a system of binary interacting chains describing the dynamics of a group of N components that, at each time unit, either send some signal to the others or remain silent otherwise. The interactions among the chains are encoded by a directed Erd & ouml;s-R & eacute;nyi random graph with unknown parameter p is an element of (0, 1). Moreover, the system is structured within two populations (excitatory chains versus inhibitory ones), which are coupled via a mean field interaction on the underlying Erd & ouml;s-R & eacute;nyi graph. In this paper, we address the question of inferring the connectivity parameter p based only on the observation of the interacting chains over T time units. In our main result, we show that the connectivity parameter p can be estimated with rate N-1/2 + N1/2/T + (log(T)/T )1/2 through an easy-to-compute estimator. Our analysis relies on a precise study of the spatiotemporal decay of correlations of the interacting chains. This is done through the study of coalescing random walks defining a backward regeneration representation of the system. Interestingly, we also show that this backward regeneration representation allows us to perfectly sample the system of interacting chains (conditionally on each realization of the underlying Erd & ouml;s-R & eacute;nyi graph) from its stationary distribution. These probabilistic results have an interest in its own.
Keywords:
Dependence graph inference
Markov chain
mean field limit
perfect simulation
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