Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Statistical mechanics of complex networks
Statistical mechanics of complex networks
Statistical Relational Learning for Document Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Automating the Mean-Field Method for Large Dynamic Gossip Networks
QEST '10 Proceedings of the 2010 Seventh International Conference on the Quantitative Evaluation of Systems
Web dynamics as a random walk: how and why power laws occur
Proceedings of the 3rd Annual ACM Web Science Conference
Toward a next generation of network models for the web
Proceedings of the 5th Annual ACM Web Science Conference
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Studies on social networks have proved that endogenous and exogenous factors influence dynamics. Two streams of modeling exist on explaining the dynamics of social networks: 1) models predicting links through network properties, and 2) models considering the effects of social attributes. In this interdisciplinary study we work to overcome a number of computational limitations within these current models. We employ a mean-field model which allows for the construction of a population-specific model informed from empirical research for predicting links from both network and social properties in large social networks.. The model is tested on a population of conference coauthorship behavior, considering a number of parameters from available Web data. We address how large social networks can be modeled preserving both network and social parameters. We prove that the mean-field model, using a data-aware approach, allows us to overcome computational burdens and thus scalability issues in modeling large social networks in terms of both network and social parameters. Additionally, we confirm that large social networks evolve through both network and social-selection decisions; asserting that the dynamics of networks cannot singly be studied from a single perspective but must consider effects of social parameters.