7 research outputs found
Segue: Overviewing Evolution Patterns of Egocentric Networks by Interactive Construction of Spatial Layouts
Getting the overall picture of how a large number of ego-networks evolve is a
common yet challenging task. Existing techniques often require analysts to
inspect the evolution patterns of ego-networks one after another. In this
study, we explore an approach that allows analysts to interactively create
spatial layouts in which each dot is a dynamic ego-network. These spatial
layouts provide overviews of the evolution patterns of ego-networks, thereby
revealing different global patterns such as trends, clusters and outliers in
evolution patterns. To let analysts interactively construct interpretable
spatial layouts, we propose a data transformation pipeline, with which analysts
can adjust the spatial layouts and convert dynamic egonetworks into event
sequences to aid interpretations of the spatial positions. Based on this
transformation pipeline, we developed Segue, a visual analysis system that
supports thorough exploration of the evolution patterns of ego-networks.
Through two usage scenarios, we demonstrate how analysts can gain insights into
the overall evolution patterns of a large collection of ego-networks by
interactively creating different spatial layouts.Comment: Published at IEEE Conference on Visual Analytics Science and
Technology (IEEE VAST 2018
Egocentric Analysis of Dynamic Networks with EgoLines
International audienceThe 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