Email and Network Analyzer

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The Email Analyzer interface helps an analyst visualize the email network and identify local group of people who frequently exchange emails amongst themselves. This interface was developed as an entry for VAST Challenge 2014. For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu

Daten & Analysen

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VAST 2014 Grand Challenge Award: Effective Analysis and Presentation
VAST 2014 Mini Challenge 2 Award: Honorable Mention for Effective Presentation
Parang Saraf; Patrick Butler; Naren Ramakrishnan
Discovery Analytics Center, Department of Computer Science, Virginia Tech
1
Presented By: Parang Saraf

System Hosted At:
http://embers.cs.vt.edu:60051
2

VAST Challenge Solution From DAC
•  The DAC solution offers three key advantages:
1.  Provides an efficient front-end interface for user-centered
exploration of data
2.  Very little analysis or cleaning of data is performed in the
backend, thereby helping an analyst to understand the
data better
!  Example: Faulty news sources or GPS coordinates are displayed
3.  Offers an intuitive interface to present data in several
different ways
•  Each Interface was designed from scratch
specifically for the VAST Challenge
3

Mini Challenge - I
•  Two Interfaces:
– News Analyzer
•  Helps an Analyst
explore news articles
and Identify Events
– Email Analyzer
•  Helps an Analyst
visualize and examine
Email network
5

Email Analyzer
26
Spectral Co-Clustering
•  Given an n x m matrix of n
documents and m words, the
algorithm performs co-clustering of
documents and words.
•  The clustering problem is posed in
terms of finding minimum cut vertex
partitions in a bipartite graph
between document and words
•  We provide an m x m matrix where
rows and columns denote
employees and a cell denotes the
number of emails exchanged
•  Implemented using the scikit-learn
package

Email Analyzer
Packages Used
•  Co-Occurrence Matrix
– Spectral Co-Clustering Algorithm
– Visualization
•  D3
–  Email Radial, Co-Occurrence matrix and Word Cloud
33

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1. Safeguarding Abila through Multiple Data Perspectives VAST 2014 Grand Challenge Award: Effective Analysis and Presentation VAST 2014 Mini Challenge 2 Award: Honorable Mention for Effective Presentation Parang Saraf; Patrick Butler; Naren Ramakrishnan Discovery Analytics Center, Department of Computer Science, Virginia Tech 1 Presented By: Parang Saraf

2. System Hosted At: http://embers.cs.vt.edu:60051 2

3. VAST Challenge Solution From DAC •  The DAC solution offers three key advantages: 1.  Provides an efficient front-end interface for user-centered exploration of data 2.  Very little analysis or cleaning of data is performed in the backend, thereby helping an analyst to understand the data better !  Example: Faulty news sources or GPS coordinates are displayed 3.  Offers an intuitive interface to present data in several different ways •  Each Interface was designed from scratch specifically for the VAST Challenge 3

4. Mini Challenge - I 4

5. Mini Challenge - I •  Two Interfaces: – News Analyzer •  Helps an Analyst explore news articles and Identify Events – Email Analyzer •  Helps an Analyst visualize and examine Email network 5

10. Email Analyzer 23

11. Email Analyzer 24

12. Email Analyzer 25

13. Email Analyzer 26 Spectral Co-Clustering •  Given an n x m matrix of n documents and m words, the algorithm performs co-clustering of documents and words. •  The clustering problem is posed in terms of finding minimum cut vertex partitions in a bipartite graph between document and words •  We provide an m x m matrix where rows and columns denote employees and a cell denotes the number of emails exchanged •  Implemented using the scikit-learn package

14. Email Analyzer 27

15. Email Analyzer 28

16. Email Analyzer 29

17. Email Analyzer - Example 30

18. Email Analyzer - Example 31

19. Email Analyzer - Example 32

20. Email Analyzer Packages Used •  Co-Occurrence Matrix – Spectral Co-Clustering Algorithm – Visualization •  D3 –  Email Radial, Co-Occurrence matrix and Word Cloud 33

21. Thank You 92

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