Abstract:
Statisticalmodelsof network structure are widely used in network science to reason about the properties of complex systems\u2014where the nodes represent entities (e.g., users) and the links represent relationships (e.g., friendships). The goal is to developgenerative network models(GNMs) that accurately capture the observed characteristics in real world networks to acquire a better understanding of the underlying properties of the system (e.g., social network). This is because a good descriptive model of social networks may not necessarily facilitate sampling. Along this area of research, it is also important to develop sampling methods for attributed networks (networks with vertex-attributes). However, this task remains a challenging problem because most current methods work with relatively simple GNMs.

In this talk, I will discuss the case ofa number of recent GNMs thatshare a common form. This structureallows to increase both the variance of the models and the space of characteristics possible to bemodeled. The structure also allows for a unified efficient sampling method.However, since in this structurethe probability mass is allocated to certain regions of the network space it is hard to identify candidate networks to sampleattributed networks. To solve this problem I will talk abouta novel sampling method, CSAG, that generatesattributed networks from GNMs equivalent to the new representation. CSAG constrains every step of the sampling process to bias the search to regions of the space with higher likelihood. The results show that CSAG jointly models the correlation and structure of the datasets better than the state of the art, and maintains the variability of the GNM while providing a reduction of 5 times or more in the error of the correlation.

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