Difference between revisions of "ISSS608 2017-18 T3 Assign Joel Choo Peng Yeow Company Conclusion"

From Visual Analytics and Applications
Jump to navigation Jump to search
 
(3 intermediate revisions by the same user not shown)
Line 22: Line 22:
 
| style="font-family:Arial; font-size:100%; solid #1B338F; background:#edf2e9; text-align:center;" width="15%" |  
 
| style="font-family:Arial; font-size:100%; solid #1B338F; background:#edf2e9; text-align:center;" width="15%" |  
 
;
 
;
[[ISSS608 2017-18 T3 Assign Joel Choo Peng YeowAre You Guilty|<b><font size="3"><font color="#000000">Are You Guilty?</font></font></b>]]
+
[[ISSS608 2017-18 T3 Assign Joel Choo Peng Yeow Are You Guilty|<b><font size="3"><font color="#000000">Are You Guilty?</font></font></b>]]
  
 
| style="font-family:Arial; font-size:100%; solid #1B338F; background:#edf2e9; text-align:center;" width="20%" |  
 
| style="font-family:Arial; font-size:100%; solid #1B338F; background:#edf2e9; text-align:center;" width="20%" |  
Line 34: Line 34:
 
|  &nbsp;
 
|  &nbsp;
 
|}
 
|}
<br>
+
===Conclusion===
 +
Through the use of network graphs and graph metrics, we are able to narrow down the suspects to the follow employees of Kasios. Depending on how stringent we would like, we could filter the list to include more employees by the '''Closeness''' Criterion.
  
===Conclusion===
+
Monitoring the network graph across time have helped us understand the organisation's structure and communication which have led to leads. Below depicts the suspects and the reason for further investigation.
Using the list of suspects provided by the insider, we would like to determine if anyone else appears to be closely related to the group and which employees are making suspicious purchase. The below depicts the original network of the list of suspicious individuals provided by the insider. The size of nodes and node labels indicates the in-degree each node appears within the dataset. Larger node represents that many edges go in the node and communications are directed at them.
 
  
 
{| class="wikitable"
 
{| class="wikitable"
Line 89: Line 89:
 
Meeting involvement<br>
 
Meeting involvement<br>
 
First Contact<br>
 
First Contact<br>
POC with Lizbeth<br>
+
Point of contact for Lizbeth to the rest of the group<br>
 
Initial Suspect & High Closeness Value<br>
 
Initial Suspect & High Closeness Value<br>
 
Initial Suspect & High Closeness Value<br>
 
Initial Suspect & High Closeness Value<br>

Latest revision as of 21:37, 8 July 2018

Joel MagnifyingGlass.png

"You See, But You Do Not Observe" - Sherlock
Looking Deeper Into The Network Of Connected Individuals

Background

Methodology

Company Growth

Are You Guilty?

Conclusion

[Back To Assignments]

 

Conclusion

Through the use of network graphs and graph metrics, we are able to narrow down the suspects to the follow employees of Kasios. Depending on how stringent we would like, we could filter the list to include more employees by the Closeness Criterion.

Monitoring the network graph across time have helped us understand the organisation's structure and communication which have led to leads. Below depicts the suspects and the reason for further investigation.

Header text Header text

Richard Fox
Meryl Pastuch
Lizbeth Jindra
Tobi Gatlin
Julie Tierno
Lindsy Henion
Rosalia Larroque
Kerstin Belveal
Sherrell Biebel
Ricky Miles
Marian Ahmadi
Loriann Gerard
Craig Carr
Chang Tulip
Timothy Gibson
Madeline Nindorf
Alex Hall
Lizbeth Jindra
Patrick Lane
Sara Ballard
May Burton
Glen Grant
Dylan Ballard
Melita Scarpaci
Augusta Sharp
Jose Ringwald
Ramiro Gault
Refugio Orrantia
Jenice Savaria

Big 4 Suspect
Big 4 Suspect
Big 4 Suspect
Big 4 Suspect
Meeting involvement
Meeting involvement
Meeting involvement
Meeting involvement
Meeting involvement
Meeting involvement
Meeting involvement
Meeting involvement
Meeting involvement
Meeting involvement
First Contact
Point of contact for Lizbeth to the rest of the group
Initial Suspect & High Closeness Value
Initial Suspect & High Closeness Value
Initial Suspect & High Closeness Value
Initial Suspect & High Closeness Value
Initial Suspect & High Closeness Value
Initial Suspect & High Closeness Value
Initial Suspect & High Closeness Value
Initial Suspect & High Closeness Value
Initial Suspect & High Closeness Value
Initial Suspect & High Closeness Value
Initial Suspect & High Closeness Value
Initial Suspect & High Closeness Value
Initial Suspect & High Closeness Value