The document summarizes a network analysis project of MSI student projects, backgrounds, and interests. Data was collected from students and analyzed to develop networks of group projects, undergraduate backgrounds, and interests. An application was built using Prefuse in Java to visualize the networks and allow interaction and exploration of relationships between students based on collaboration history, shared tags, and other attributes. The application features ego-graphs, aggregation by specialization, loading random graphs, and viewing recent graphs.

1. Network Analysis of MSI Student
projects and keywords
Zhe (Jo) Pu
Chutimon Sindhuprama

2. Introduction: Motivation
 SI students find partners for their class projects
 Annual campus-wide GROCS competition
 Students would like to initiate study groups or
social activities

3. Methodology
 Data Collection
 Data Cleaning
 Analyze the Networks
 Developing Application

4. Data Collection and Cleaning

5. Network Analysis
 Students – Group Projects
 Students – Undergraduate Backgrounds
 Students - Interests

6. Students – Group Projects

7. Students – Group Projects [contʼ]
Specializations Gender

8. Students – Group Projects [contʼ]

9. Students – Undergraduate Backgrounds

10. Students – Undergraduate Backgrounds [contʼ]
Specializations

11. Students – Undergraduate Backgrounds [contʼ]

12. Students – Undergraduate Backgrounds [contʼ]

13. Students – Interests

14. Students – Interests [contʼ]
Specializations Gender

15. Students – Interests [contʼ]

16. Students – Interests [contʼ]

17. Application
Built with Prefuse in Java
Step 1: data retrieval from the MySQL database
Step 2: graph construction with data fetched on the fly
Step 3: the definitions for the look and feel of the graph
Step 4: UI elements that allows interaction and
exploration with the network

18. Application -- features
• Ego-graph with two tiers of vertices
1. Who has collaborated with this person in the past;
2. Roughly how many times two students have collaborated
before;
3. What tags this person and his/her past teammates use to
describe themselves; and
4. Who also shared a same tag to this person and his/her
collaborators.
• Aggregation by specialization
The shrinking effect

19. Application – features (contʼd)
• Loading random graph as support of browsing
• View recent graphs as support of browsing
With a pre-defined maximum capacity of recently viewed graphs

20. Application -- demo

21. Thank you very much!
Questions?