GraphDice: A System for Exploring Multivariate Social Networks

Jun 30, 2010Download as pptx, pdf0 likes676 views

This document describes GraphDice, a system for exploring multivariate social networks. GraphDice allows users to visualize social networks, with nodes representing individuals and edges representing relationships. It integrates network topology, node and edge attributes, and tabular data views. GraphDice is designed for social network analysts to consistently represent and interact with network data through features like dynamic queries, selection history, and coordinated visualizations and data tables.

1GraphDiceA System for Exploring Multivariate Social NetworksDagstuhl editionPierre Dragicevic- The Good -Jean-Daniel Fekete- The Ugly -Anastasia BezerianosNiklasElmqvist- The Bad -Fanny Chevalier

2Social Network AnalysisIntrinsicattributes # publications

authoring position…Computedattributesdegree

betweenesscentralityclustering coefficient…3Social Network AnalysisIntrinsicattributes # publications

betweenesscentralityclustering coefficient…4MotivationTarget Users: Social network analystsDesign Rationale:Consistent and simple representation and interactionEdge and node attribute visualizationLink between tabular data and vis

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Slides for the session delivered at Devoxx UK 2025 - Londo. Discover how to seamlessly integrate AI LLM models into your website using cutting-edge techniques like new client-side APIs and cloud services. Learn how to execute AI models in the front-end without incurring cloud fees by leveraging Chrome's Gemini Nano model using the window.ai inference API, or utilizing WebNN, WebGPU, and WebAssembly for open-source models. This session dives into API integration, token management, secure prompting, and practical demos to get you started with AI on the web. Unlock the power of AI on the web while having fun along the way!

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GraphDice: A System for Exploring Multivariate Social Networks

1. 1GraphDiceA System for Exploring Multivariate Social NetworksDagstuhl editionPierre Dragicevic- The Good -Jean-Daniel Fekete- The Ugly -Anastasia BezerianosNiklasElmqvist- The Bad -Fanny Chevalier

2. 2Social Network AnalysisIntrinsicattributes # publications

3. # citations

4. year

5. authoring position…Computedattributesdegree

6. betweenesscentralityclustering coefficient…3Social Network AnalysisIntrinsicattributes # publications

7. # citations

8. year

9. authoring position…Computedattributesdegree

10. betweenesscentralityclustering coefficient…4MotivationTarget Users: Social network analystsDesign Rationale:Consistent and simple representation and interactionEdge and node attribute visualizationLink between tabular data and vis

11. 5GraphDice Interface

12. 6GraphDice Interface

13. 7GraphDice Interface

14. Control Menu8

15. 9Dynamic Queries

16. 10Selection History

17. Transient Buttons11

18. Jitter12

19. Zoom13

20. Zoom14

21. User Feedback15“Easy to use, but it took a bit to understand edge attributes”“have all combinations of attributes immediately available to quickly explore hypothesis”“can be used to communicate findings”“animation helps to compare attributes”“tables give security and confidence that I am looking at the correct data”

22. Directed Networks16

23. Data Tables17

24. Conclusion18Unified representation oftopology-based layout / cartographic positions

25. intrinsic attributes

26. computed measures

27. edge attributesConsistent and visible interactionHistory managementCoordinated visual and tabular representations

28. Thankyouwww.aviz.fr/graphdice19