Predictors of short-term decay of cell phone contacts in a large scale communication network

Temporal Patterns of Communication in Social Networks

“…This means that, when we focus on a certain time window, the connections of a given individual are not active all the time, while they form and decay at a very high pace (Hidalgo and Rodriguez-Sickert 2008;Raeder et al 2011). As a consequence, the aggregate social connectivity does not capture the instantaneous contact network an individual interacts with and it may only constitute a upper limit to the real social capacity.…”

“…The most exemplary work on edge decay in social networks is probably that of Burt who studied the social networks of the most important bankers over time and analyzed those factors that contribute to the disappearance of edges between them (Burt 2000;Burt 2002) . Several other studies have focused on link characterization in other communication networks as phone and SMS networks (Akoglu and Dalvi 2010;Hidalgo and Rodriguez-Sickert 2008;Raeder et al 2011), online networks (Aiello et al 2010;Crandall et al 2008;Gilbert and Karahalios 2009;Kivran-Swaine et al 2011;Romero et al 2011) and off-line settings (Burt 2000;Martin and Yeung 2006). One of the main substantive conclusions that emerge from these studies is that links exhibit a memory, meaning that old links are more likely to persist in time than newlyformed ones (where the "age" and persistence of a link is defined in terms of observed communication events or when the link is registered or deleted in on-line social networks).…”

“…One of the main substantive conclusions that emerge from these studies is that links exhibit a memory, meaning that old links are more likely to persist in time than newlyformed ones (where the "age" and persistence of a link is defined in terms of observed communication events or when the link is registered or deleted in on-line social networks). Several other factors influence edge decay/persistence including structural properties of ties as reciprocation, neighborhood overlap or clustering coefficient, temporal properties as the time since the last communication (Raeder et al 2011), homophily (Crandall et al 2008) or geographical aspects as individual location and distance (Liben-Nowell et al 2005;Lee et al 2009). Tie creation or decay may also signal changes in the community structure and the condition for stability for large communities is strictly related to the continuous changes in their membership ).…”

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).…”

Newton’s Gravitational Law for Link Prediction in Social Networks

“…However, application of link prediction is much wider than in the above example. Link prediction has also been used to predict potential collaborators in business [32], in shopping recommendation [28], in patent partner recommendation [46], in predicting cell phone contact [38], in gene expression networks [7], in protein-protein interactions [2] and, in security related domain to detect suspicious communication [31].…”