Synchronization properties of cyber behaviors

In recent years the internet has facilitated an explosion of growth in social networks, allowing individuals to interact with one another in a variety of different contexts. Interactions between individuals in networks such as twitter and NASDAQ produce events which co-occur in time. If we make the assumption that events in networks are anonymized such that there is no mapping from the event back to the individual who produced it, we are left with a data stream consisting of spatially and temporally interleaved events with no attribution. We model this property of event co-occurrence in order to recreate this mapping by assuming a strong coupling between temporal co-occurrence and spatial variance an arbitrary individual's behavior. We present a few algorithms based on this model, which produce partitions of tracks, where each track is indicative of the behaviors from a single individual in the network. Results using the algorithms indicate that the models are valid showing a high degree of spatio-temporal consistency among behaviors in networks. This suggests the need for further exploration of new behavior models and algorithms centered around this property.