  Recent years saw an increased interest in modeling and understanding the
mechanisms of opinion and innovation spread through human networks. Using
analysis of real-world social data, researchers are able to gain a better
understanding of the dynamics of social networks and subsequently model the
changes in such networks over time. We developed a social network model that
both utilizes an agent-based approach with a dynamic update of opinions and
connections between agents and reflects opinion propagation and structural
changes over time as observed in real-world data. We validate the model using
data from the Social Evolution dataset of the MIT Human Dynamics Lab describing
changes in friendships and health self-perception in a targeted student
population over a nine-month period. We demonstrate the effectiveness of the
approach by predicting changes in both opinion spread and connectivity of the
network. We also use the model to evaluate how the network parameters, such as
the level of `openness' and willingness to incorporate opinions of neighboring
agents, affect the outcome. The model not only provides insight into the
dynamics of ever changing social networks, but also presents a tool with which
one can investigate opinion propagation strategies for networks of various
structures and opinion distributions.
