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Modeling Temporal Activity Patterns in Dynamic Social Networks
Activity Profile Modeling Twitter Data-Fitting Explanation Prediction Hidden Markov Model Coupled Hidden Markov Model Social Network In uence User Clustering
2013/6/14
The focus of this work is on developing probabilistic models for user activity in social networks by incorporating the social network influence as perceived by the user. For this, we propose a coupled...
Majority Dynamics and Aggregation of Information in Social Networks
Majority Dynamics Aggregation of Information Social Networks
2012/9/18
Considernindividuals who, by popular vote, choose among q≥2 alternatives, one of which is “better” than the others. Assume that each individual votes independently at random, and that the probability ...
Co-evolution of Selection and Influence in Social Networks
Co-evolution Selection Influence Social Networks
2011/7/7
Many networks are complex dynamical systems, where both attributes of nodes and topology of the network (link structure) can change with time. We propose a model of co-evolving networks where both nod...
Social networks and industrial geography
Social networks Entrepreneurship Agglomeration Spin-offs
2010/11/5
In many industries, production resides in a small number of highly concentrated
regions; for example, several high tech industries cluster in SiliconValley.
Explanations for this phenomenon have foc...
Infinite Hierarchical MMSB Model for Nested Communities/Groups in Social Networks
Infinite Hierarchical MMSB Model Nested Communities/Groups Social Networks
2010/10/19
Actors in realistic social networks play not one but a number of diverse roles depending on whom they interact with, and a large number of such role-specific interactions collectively determine social...
Modeling social networks from sampled data
Exponential family random graph model pmodel Markov chain Monte Carlo design-based inference
2010/10/19
Network models are widely used to represent relational information among interacting units and the structural implications of these relations. Recently, social network studies have focused a great dea...
Efficient Bayesian Learning in Social Networks with Gaussian Estimators
Efficient Bayesian Learning Social Networks Gaussian Estimators
2010/3/10
We propose a simple and efficient Bayesian model of iterative learning on social networks.
This model is efficient in two senses: the process both results in an optimal belief, and can
be carried ou...
Mapping Social Networks among Crystallographers in South Africa
knowledge transfer information communication social networks crystallographers scientists
2009/7/16
The author adopts the premise that technological innovation, a critical factor in the long-term economic growth of any country, can only function successfully within a social environment that provides...