Combinatorial Inference for Graphical Models
This addresses the need for combinatorial inference methods in fields like network analysis, though it appears incremental as an extension of statistical inference to graph structures.
The paper tackles the problem of testing global graph structures in graphical models, developing a unified theory for fundamental limits and proposing matching structural testing algorithms. It demonstrates practical utility through numerical experiments on synthetic models and brain networks.
We propose a new family of combinatorial inference problems for graphical models. Unlike classical statistical inference where the main interest is point estimation or parameter testing, combinatorial inference aims at testing the global structure of the underlying graph. Examples include testing the graph connectivity, the presence of a cycle of certain size, or the maximum degree of the graph. To begin with, we develop a unified theory for the fundamental limits of a large family of combinatorial inference problems. We propose new concepts including structural packing and buffer entropies to characterize how the complexity of combinatorial graph structures impacts the corresponding minimax lower bounds. On the other hand, we propose a family of novel and practical structural testing algorithms to match the lower bounds. We provide thorough numerical results on both synthetic graphical models and brain networks to illustrate the usefulness of these proposed methods.