7,155 research outputs found
The Dynamics of a Mobile Phone Network
The empirical study of network dynamics has been limited by the lack of
longitudinal data. Here we introduce a quantitative indicator of link
persistence to explore the correlations between the structure of a mobile phone
network and the persistence of its links. We show that persistent links tend to
be reciprocal and are more common for people with low degree and high
clustering. We study the redundancy of the associations between persistence,
degree, clustering and reciprocity and show that reciprocity is the strongest
predictor of tie persistence. The method presented can be easily adapted to
characterize the dynamics of other networks and can be used to identify the
links that are most likely to survive in the future
A Relational Event Approach to Modeling Behavioral Dynamics
This chapter provides an introduction to the analysis of relational event
data (i.e., actions, interactions, or other events involving multiple actors
that occur over time) within the R/statnet platform. We begin by reviewing the
basics of relational event modeling, with an emphasis on models with piecewise
constant hazards. We then discuss estimation for dyadic and more general
relational event models using the relevent package, with an emphasis on
hands-on applications of the methods and interpretation of results. Statnet is
a collection of packages for the R statistical computing system that supports
the representation, manipulation, visualization, modeling, simulation, and
analysis of relational data. Statnet packages are contributed by a team of
volunteer developers, and are made freely available under the GNU Public
License. These packages are written for the R statistical computing
environment, and can be used with any computing platform that supports R
(including Windows, Linux, and Mac).
Temporal patterns behind the strength of persistent ties
Social networks are made out of strong and weak ties having very different structural and dynamical properties. But what features of human interaction build a strong tie? Here we approach this question from a practical way by finding what are the properties of social interactions that make ties more persistent and thus stronger to maintain social interactions in the future. Using a large longitudinal mobile phone database we build a predictive model of tie persistence based on intensity, intimacy, structural and temporal patterns of social interaction. While our results confirm that structural (embeddedness) and intensity (number of calls) features are correlated with tie persistence, temporal features of communication events are better and more efficient predictors for tie persistence. Specifically, although communication within ties is always bursty we find that ties that are more bursty than the average are more likely to decay, signaling that tie strength is not only reflected in the intensity or topology of the network, but also on how individuals distribute time or attention across their relationships. We also found that stable relationships have and require a constant rhythm and if communication is halted for more than 8 times the previous communication frequency, most likely the tie will decay. Our results not only are important to understand the strength of social relationships but also to unveil the entanglement between the different temporal scales in networks, from microscopic tie burstiness and rhythm to macroscopic network evolution.EM acknowledges funding from Ministerio de Economía y Competividad (Spain) through projects FIS2013-47532-C3-3-P and FIS2016-78904-C3-3-P
Influence of Personal Preferences on Link Dynamics in Social Networks
We study a unique network dataset including periodic surveys and electronic
logs of dyadic contacts via smartphones. The participants were a sample of
freshmen entering university in the Fall 2011. Their opinions on a variety of
political and social issues and lists of activities on campus were regularly
recorded at the beginning and end of each semester for the first three years of
study. We identify a behavioral network defined by call and text data, and a
cognitive network based on friendship nominations in ego-network surveys. Both
networks are limited to study participants. Since a wide range of attributes on
each node were collected in self-reports, we refer to these networks as
attribute-rich networks. We study whether student preferences for certain
attributes of friends can predict formation and dissolution of edges in both
networks. We introduce a method for computing student preferences for different
attributes which we use to predict link formation and dissolution. We then rank
these attributes according to their importance for making predictions. We find
that personal preferences, in particular political views, and preferences for
common activities help predict link formation and dissolution in both the
behavioral and cognitive networks.Comment: 12 page
Friendship Between Banks: An Application of an Actor-Oriented Model of Network Formation on Interbank Credit Relations
This paper investigates the driving forces behind banks' link formation in the interbank market by applying the stochastic actor oriented model (SAOM) developed in sociology. Our data consists of quarterly networks constructed from the transactions on an electronic platform (e-MID) over the period from 2001 to2010. Estimating the model for the time before and after the global financial crisis (GFC), shows relatively similar behavior over the complete period. We find that past trades are a significant predictor of future credit relations which indicates a strong role for the formation of lasting relationships between banks. We also find strong importance of size-related characteristics, but little influence of past interest rates. The major changes found for the period after the onset of the financial crisis are that: (1) large banks and those identified as 'core' intermediaries became even more popular and (2) indirect counterparty risk appears to be more of a concern as indicated by a higher tendency to avoid indirect exposure via clustering effects
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