Social-Based Classification of Multiple Interactions in Dynamic Attributed Networks

How to classify the dynamic interactions in social networks? We address this task by exploring the behavior and the dynamics of the actors in a social network. Specifically, we reinforce the importance of social concepts to capture the social meaning of relationships. Then, we propose a new strategy to classify nodes and dynamic edges based on node-attribute relationships. We apply our proposal to two attribute scenarios: tokens associated with edges and social ties associated with communities. As a result, we unveil the differences of social relationships in different academic social network scenarios. Our method differs from an existing one by defining more elaborated classes in terms of social concepts and performing 7.7 times faster than it. Finally, we validate our social definitions in terms of the best positioned nodes according to network metrics, as well as investigate the robustness of our method.