Temporal Patterns of Communication in Social Networks

Temporal patterns behind the strength of persistent ties

“…To mitigate this problem, we only keep active users in our data set: in particular, we only consider those users who are involved (as calling or as called party) at least in one communication event in each of the three subintervals in the 19 months and also if they are present in the database at least one month before Ω and are still active one month after Ω. This latter filter prevents spurious effects in the analysis of tie dynamics just because individuals subscribe/unsubscribe just before/after Ω; for example, we could have observed an apparent rapid growth of their social network at the beginning of the observation window or a fast dissolution at its end [24]. This results in the removal of about the 17% of nodes and the 37% of reciprocated links within Ω.…”

“…S OCIAL networks are dynamic objects, they grow and change over time through the addition of new ties or the removal of old ones, leading to an ongoing appearance and disappearance of interactions in the underlying social structure [16,35]. Identifying the different mechanisms by which a tie form or decay is a fundamental and challenging question of individual human behavior, but also it can unravel the processes behind group, community and network dynamics that shape our social fabric and, in turn, how that network evolution impact important processes in our society like cooperation [32], disease spreading [15] or information diffusion [18,24,26]. On the other hand, understanding under what condition a tie is more or less likely to decay may shed light on the circumstances under which an observed interaction can be actually considered a genuine social relationship [14,19] and its present and future potential strength in the different processes happening in social networks.…”

Birth and death of links control disease spreading in empirical contact networks

“…Since temporal network data of human behavior is difficult to gather, we use the not-so-relevant data sets too. These are also interesting in a larger context of patterns of human activities27, specifically for studies of disease spreading in electronic media1228. After further motivating the study by investigating the statistics of the interevent-, beginning and end intervals for contacts between pairs, we simulate epidemic outbreaks on these data sets.…”

On the research for big data uses for public good purposes

“…Looking at the network with a dynamic point of view has shown that in many cases, the appearance or decay of relationships between people in the network can be predicted when sufficient information is available on past contacts (Raeder et al, 2011, Miritello, 2013. Furthermore, despite a turnover in links and in the network surrounding individuals, the structure of the network and the distribution of link weights around a person remains very similar through time, representing a type of social signature of a person (Saramäki et al, 2014).…”