A Regularization Framework for Mobile Social Network Analysis

Mobile phone data provides rich dynamic information on human activities in social network analysis. In this paper, we represent data from two different modalities as a graph and functions defined on the vertex set of the graph. We propose a regularization framework for the joint utilization of these two modalities of data, which enables us to model evolution of social network information and efficiently classify relationships among mobile phone users. Simulations based on real world data demonstrate the potential application of our model in dynamic scenarios, and present competitive results to baseline methods for combining multimodal data in the learning and clustering communities.


Published in:
Proceedings of the 36th International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Presented at:
International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, May 22-27, 2011
Year:
2011
Keywords:
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 Record created 2010-11-29, last modified 2018-09-13

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