Egocentric Analysis of Dynamic Networks with EgoLines

Jian Zhao 1 Michael Glueck 1 Fanny Chevalier 2 Yanhong Wu 3 Azam Khan 1
2 MJOLNIR - Computing tools to empower users
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : The egocentric analysis of dynamic networks focuses on discovering the temporal patterns of a subnetwork around a specific central actor (i.e., an ego-network). These types of analyses are useful in many application domains, such as social science and business intelligence, providing insights about how the central actor interacts with the outside world. We present EgoLines, an interactive visualization to support the egocentric analysis of dynamic networks. Using a "subway map" metaphor, a user can trace an individual actor over the evolution of the ego-network. The design of EgoLines is grounded in a set of key analytical questions pertinent to egocentric analysis, derived from our interviews with three domain experts and general network analysis tasks. We demonstrate the effectiveness of EgoLines in egocentric analysis tasks through a controlled experiment with 18 participants and a use-case developed with a domain expert.
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Jian Zhao, Michael Glueck, Fanny Chevalier, Yanhong Wu, Azam Khan. Egocentric Analysis of Dynamic Networks with EgoLines. CHI '16 - Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems , ACM, May 2016, San Jose, CA, United States. pp.5003-5014 ⟨10.1145/2858036.2858488⟩. ⟨hal-01325788⟩

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