Higher-Order Graphon Theory: Fluctuations, Inference, and Degeneracies

          @misc{ scivideos_22593,
            doi = {},
            url = {https://old.simons.berkeley.edu/node/22593},
            author = {},
            keywords = {},
            language = {en},
            title = {Higher-Order Graphon Theory: Fluctuations, Inference, and Degeneracies},
            publisher = {The Simons Institute for the Theory of Computing},
            year = {2022},
            month = {sep},
            note = {22593 see, \url{https://scivideos.org/index.php/simons-institute/22593}}
          }
          

Abstract

Abstract Motifs (patterns of subgraphs), such as edges and triangles, encode important structural information about the geometry of a network. Consequently, counting motifs in a large network is an important statistical and computational problem. In this talk we will consider the problem of estimating motif densities and fluctuations of subgraph counts in an inhomogeneous random graph sampled from a graphon. We will show that the limiting distributions of subgraph counts can be Gaussian or non-Gaussian, depending on a notion of regularity of subgraphs with respect to the graphon. Using these results and a novel multiplier bootstrap for graphons, we will construct joint confidence sets for the motif densities. Finally, we will discuss various structure theorems and open questions about degeneracies of the limiting distribution. Joint work with Anirban Chatterjee, Soham Dan, and Svante Janson.