Difference between revisions of "ISSS608 2017-18 T3 Assign Joel Choo Peng Yeow Company Conclusion"
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− | + | ===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. | |
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− | + | 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. | |
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− | | | + | {| class="wikitable" |
− | + | |- | |
− | + | ! Header text !! Header text | |
+ | |- | ||
+ | | | ||
+ | Richard Fox <br> | ||
+ | Meryl Pastuch<br> | ||
+ | Lizbeth Jindra<br> | ||
+ | Tobi Gatlin<br> | ||
+ | Julie Tierno<br> | ||
+ | Lindsy Henion<br> | ||
+ | Rosalia Larroque<br> | ||
+ | Kerstin Belveal<br> | ||
+ | Sherrell Biebel<br> | ||
+ | Ricky Miles<br> | ||
+ | Marian Ahmadi<br> | ||
+ | Loriann Gerard<br> | ||
+ | Craig Carr<br> | ||
+ | Chang Tulip<br> | ||
+ | Timothy Gibson<br> | ||
+ | Madeline Nindorf<br> | ||
+ | Alex Hall<br> | ||
+ | Lizbeth Jindra<br> | ||
+ | Patrick Lane<br> | ||
+ | Sara Ballard<br> | ||
+ | May Burton<br> | ||
+ | Glen Grant<br> | ||
+ | Dylan Ballard<br> | ||
+ | Melita Scarpaci<br> | ||
+ | Augusta Sharp<br> | ||
+ | Jose Ringwald<br> | ||
+ | Ramiro Gault<br> | ||
+ | Refugio Orrantia<br> | ||
+ | Jenice Savaria<br> | ||
+ | || | ||
+ | Big 4 Suspect<br> | ||
+ | Big 4 Suspect<br> | ||
+ | Big 4 Suspect<br> | ||
+ | Big 4 Suspect<br> | ||
+ | Meeting involvement<br> | ||
+ | Meeting involvement<br> | ||
+ | Meeting involvement<br> | ||
+ | Meeting involvement<br> | ||
+ | Meeting involvement<br> | ||
+ | Meeting involvement<br> | ||
+ | Meeting involvement<br> | ||
+ | Meeting involvement<br> | ||
+ | Meeting involvement<br> | ||
+ | Meeting involvement<br> | ||
+ | First Contact<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> | ||
+ | Initial Suspect & High Closeness Value<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> | ||
+ | Initial Suspect & High Closeness Value<br> | ||
+ | Initial Suspect & High Closeness Value<br> | ||
+ | Initial Suspect & High Closeness Value<br> | ||
+ | Initial Suspect & High Closeness Value<br> | ||
+ | |} | ||
+ | <br> | ||
− | + | <hr/> | |
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| 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%" | |
Revision as of 21:35, 8 July 2018
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 |
Big 4 Suspect |
| 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%" |
|
|}
Conclusion
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.
Header text | Header text |
---|---|
Richard Fox |
Big 4 Suspect |