Modeling Temporal Activity Patterns in Dynamic Social Networks
نویسندگان
چکیده
منابع مشابه
Temporal Fidelity in Dynamic Social Networks
It has recently become possible to record detailed social interactions in large social systems with high resolution. As we study these datasets, human social interactions display patterns that emerge at multiple time scales, from minutes to months. On a fundamental level, an understanding of the network dynamics can be used to inform the process of measuring social networks. The details of meas...
متن کاملTemporal Patterns of Activity in Neural Networks
Patterns of activity over real neural structures are known to exhibit timedependent behavior. It would seem that the brain may be capable of utilizing temporal behavior of activity in neural networks as a way of performing functions which cannot otherwise be easily implemented. These might include the origination of sequential behavior and the recognition of time-dependent stimuli. A model is p...
متن کاملActivity driven modeling of dynamic networks
Nicola Perra, Bruno Gonçalves, Romualdo Pastor-Satorras, Alessandro Vespignani Department of Physics, College of Computer and Information Sciences, Department of Health Sciences, Northeastern University, Boston MA 02115 USA Linkalab, Cagliari, Italy Departament de F́ısica i Enginyeria Nuclear, Universitat Politècnica de Catalunya, Campus Nord B4, 08034 Barcelona, Spain Institute for Scientific I...
متن کاملDiscovery of Spatio-Temporal Patterns from Location Based Social Networks
Location Based Social Networks (LBSN) like Twitter or Instagram are a good source for user spatio-temporal behavior. These networks collect data from users in such a way that they can be seen as a set of collective and distributed sensors of a geographical area. A low rate sampling of user’s location information can be obtained during large intervals of time that can be used to discover complex...
متن کاملMining Frequent Spatio-Temporal Patterns from Location Based Social Networks
Location Based Social Networks (LBSN) like Twitter or Instagram are a good source for user spatio-temporal behavior. These social network provide a low rate sampling of user’s location information during large intervals of time that can be used to discover complex behaviors, including frequent routes, points of interest or unusual events. This information is important for different domains like...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Computational Social Systems
سال: 2014
ISSN: 2329-924X
DOI: 10.1109/tcss.2014.2307453