Behavior modeling with probabilistic context free grammars

Identifying the behavioral patterns in a social network setting is beneficial to understand how people behave in certain application domains. Such patterns can also be utilized to characterize social signals such as social roles from interactions. In this work, we examine how probabilistic context free grammars (PCFGs) can be utilized to model interactions and role taking in a social network. We describe how to automatically build a PCFG given a set of interactions as the training data. Our experiments on the Mission Survival Corpus 1 (MSC-1) dataset show that PCFGs are a concise way of modeling social entity behaviors and are useful in understanding the probability distribution of interactions as well as the behavior types that are observed.