Abstract
The coming big data era calls for new methodologies to process and analyze the huge volumes of data. Visual analytics is becoming increasingly crucial in data analysis, presentation, and exploring. Communication data is significant in studying human interactions and social relationships. In this paper, we propose a visual analytics system named egoPortray to interactively analyze the communication data based on directed weighted ego network model. Ego network (EN) is composed of a centered individual, its direct contacts (alters), and the interactions among them. Based on the EN model, egoPortray presents an overall statistical view to grasp the entire EN features distributions and correlations, and a glyph-based group view to illustrate the key EN features for comparing different egos. The proposed system and the idea of ego network can be generalized and applied in other fields where network structure exits.
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Acknowledgments
The authors acknowledge Zhi-Yao Teng for useful discussions. This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 61302077, 61421061, 61433014, 11222543 and 61502083) and the Key Project of the National Research Program of China (Grant No. 2012BAH41F03). Qing Wang acknowledges the joint cooperation project of BUPT and China Telecom Beijing Institute.
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Wang, Q., Pu, J., Guo, Y., Hu, Z., Tian, H. (2017). egoPortray: Visual Exploration of Mobile Communication Signature from Egocentric Network Perspective. In: Amsaleg, L., Guðmundsson, G., Gurrin, C., Jónsson, B., Satoh, S. (eds) MultiMedia Modeling. MMM 2017. Lecture Notes in Computer Science(), vol 10132. Springer, Cham. https://doi.org/10.1007/978-3-319-51811-4_53
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