Date: 2015-03-20

Time: 15:30-16:30

Location: BURN 1205

Abstract:

Networks provide a useful means to summarize sparse yet structured massive datasets, and so are an important aspect of the theory of big data. A key question in this setting is to test for the significance of community structure or what in social networks is termed homophily, the tendency of nodes to be connected based on similar characteristics. Network models where a single parameter per node governs the propensity of connection are popular in practice, because they are simple to understand and analyze. They frequently arise as null models to indicate a lack of community structure, since they cannot readily describe the division of a network into groups of nodes whose aggregate links behave in a block-like manner. Here we discuss asymptotic regimes under families of such models, and show their potential for enabling hypothesis tests in this setting. As an important special case, we treat network modularity, which summarizes the difference between observed and expected within-community edges under such null models, and which has seen much success in practical applications of large-scale network analysis. Our focus here is on statistical rather than algorithmic properties, however, in order to yield new insights into the canonical problem of testing for network community structure.

Speaker

Beate Franke is a PhD student in the Department of Statistical Science at University College London.