Graph Visualization and Navigation in Information Visualization: A Survey
IEEE Transactions on Visualization and Computer Graphics
Designing Pixel-Oriented Visualization Techniques: Theory and Applications
IEEE Transactions on Visualization and Computer Graphics
Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
Pixel bar charts: a visualization technique for very large multi-attribute data sets
Information Visualization
ASK-GraphView: A Large Scale Graph Visualization System
IEEE Transactions on Visualization and Computer Graphics
Social ties and their relevance to churn in mobile telecom networks
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
GrouseFlocks: Steerable Exploration of Graph Hierarchy Space
IEEE Transactions on Visualization and Computer Graphics
Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines
IEEE Transactions on Visualization and Computer Graphics
Detecting Highly Overlapping Communities with Model-Based Overlapping Seed Expansion
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Tugging Graphs Faster: Efficiently Modifying Path-Preserving Hierarchies for Browsing Paths
IEEE Transactions on Visualization and Computer Graphics
Visual Analysis of Large Graphs Using (X,Y)-Clustering and Hybrid Visualizations
IEEE Transactions on Visualization and Computer Graphics
EgoNav: exploring networks through egocentric spatializations
Proceedings of the International Working Conference on Advanced Visual Interfaces
Hi-index | 0.00 |
In this paper, we present ChurnVis, a system for visualizing components affected by mobile telecommunications churn and subscriber actions over time. We describe our experience of deploying this system in a network analytics company for use in data analysis and presentation tasks. As social influence seems to be a factor in mobile telecommunications churn (the decision of a subscriber to leave a particular service provider), the visualization is based on a social network inferred from calling data between subscribers. Using this network, churn components, or groups of churners who are connected in the social network, are segmented out and trends in their static and dynamic attributes are visualized. ChurnVis helps analysts understand trends in these components in a way that respects the data privacy constraints of the service provider. Through this two pipeline approach, we are able to visualize thousands of churn components filtered from a social network of hundreds of millions of edges.