Motif Estimation, Polynomial Chaos, and the Ising Model

Explore a comprehensive mathematical colloquium examining the intersection of network sampling, statistical inference, and polynomial chaos theory in complex systems. Delve into a unified framework for statistical inference when counting motifs such as edges, triangles, and wedges within subgraph sampling models, where natural estimates can be expressed as polynomial chaos functions with Gaussian fluctuations governed by fourth-moment phenomena. Investigate the special case of quadratic chaos, which emerges in diverse contexts including Ising model Hamiltonians, non-parametric statistical tests, and random graph-coloring problems, with a complete characterization of all possible distributional limits. Discover how these theoretical results apply to estimation problems in Ising models on inhomogeneous random graphs, providing insights into network sampling as an essential tool for understanding large-scale complex networks where comprehensive node querying is impractical. Learn from expert analysis of how polynomial chaos theory bridges statistical inference and network analysis, offering new perspectives on motif estimation in complex network structures.