MINING AND TESTING ON HIERARCHICAL STOCHASTIC BLOCK MODELS

Abstract

The rise in complexity of network data in neuroscience, social networks, and protein-protein interaction networks has been accompanied by efforts to model and understand these data on different scales. A key multiscale network modeling technique posits a hier- archical structure in the network. One such example of hierarchical modeling is the hierar- chical stochastic blockmodel, which seeks to model complex networks as being composed of community structures repeated across the network. Incorporating repeated structure al- lows for parameter tying across communities, reducing the model complexity compared to the traditional block model. In this work, we describe a model that naturally expresses net- works as a hierarchy of sub-networks with a set of motifs repeating across it and formally define the subgraph nomination framework with an emphasis on the notion of a user-in- the-loop in the subgraph nomination pipeline.