Network Representation Using Graph Root Distributions

Exchangeable random graphs serve as an important probabilistic framework for the statistical analysis of network data. This work introduces an alternative parameterization for a large class of exchangeable random graphs, where the nodes are independent random vectors in a linear space equipped with an indefinite inner product, and the edge probability between two nodes equals the inner product of the corresponding node vectors. Therefore, the distribution of exchangeable random graphs in this subclass can be represented by a node sampling distribution on this linear space, which we call the “graph root distribution”. We study existence and identifiability of such representations, the topological relationship between the graph root distribution and the exchangeable random graph sampling distribution, and estimation of graph root distributions.

• Speaker: Jing Lei, Carnegie Mellon University
• Friday 15 May 2020, 14:00–15:00
• Venue: https://zoom.us/j/95022384263?pwd=N3Z6elB2Vy9Jajd6azlCNjFHQVlKdz09.
• Series: Statistics; organiser: Dr Sergio Bacallado.