Tracing genealogical data with TimeNets
Proceedings of the International Conference on Advanced Visual Interfaces
Multi-con: exploring graphs by fast switching among multiple contexts
Proceedings of the International Conference on Advanced Visual Interfaces
Visual coder: clinical coding in translational research
Proceedings of the 1st ACM International Health Informatics Symposium
Apolo: making sense of large network data by combining rich user interaction and machine learning
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Visual web mining for website evaluation
Journal of Web Engineering
Apolo: interactive large graph sensemaking by combining machine learning and visualization
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Interactive dynamics for visual analysis
Communications of the ACM
Interactive Dynamics for Visual Analysis
Queue - Micoprocessors
Using Signposts for Navigation in Large Graphs
Computer Graphics Forum
PaperVis: literature review made easy
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Dynamic insets for context-aware graph navigation
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Visual recommendations for network navigation
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Multifaceted visual analytics for healthcare applications
IBM Journal of Research and Development
Knot: an interface for the study of social networks in the humanities
Proceedings of the Biannual Conference of the Italian Chapter of SIGCHI
Guiding the interactive exploration of metabolic pathway interconnections
Information Visualization
Hi-index | 0.02 |
A common goal in graph visualization research is the design of novel techniques for displaying an overview of an entire graph. However, there are many situations where such an overview is not relevant or practical for users, as analyzing the global structure may not be related to the main task of the users that have semi-specific information needs. Furthermore, users accessing large graph databases through an online connection or users running on less powerful (mobile) hardware simply do not have the resources needed to compute these overviews. In this paper, we advocate an interaction model that allows users to remotely browse the immediate context graph around a specific node of interest. We show how Furnas’ original degree of interest function can be adapted from trees to graphs and how we can use this metric to extract useful contextual subgraphs, control the complexity of the generated visualization and direct users to interesting datapoints in the context. We demeffectiveness of our approach with an exploration of a dense online database containing over 3 million legal citations.