1. Optimizing Significant Node Discovery in Multilayer Networks
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Optimizing Significant Node Discovery in Multilayer Networks
SEAP Student:
Kushagro Bhattacharjee
Saratoga High School
Class of 2017
Mentor:
Dr. Ralucca Gera
Department of Applied Mathematics and Center for Cyber Warfare
Project Dates: 20 June – 12 August
Project Objective and Research Approach:
In social networks, some people are friendly and some are the enemy or at least people of interest. Both groups of people interact (communication, activities, financials…). Can we use the interactions to make any determinations about the enemy footprint in the area of operations?
Approach: Created and implemented an algorithm to identify target nodes within multilayered social networks: Multilayered Connotation Algorithm for Discovery (MultiConn)
Results / Accomplishments
The algorithm we introduced, MultiConn:
prints out a list of the nodes of interest based on their negative connotation
plots the Nodes of Interest found vs. the Number of Monitors gone through for multiple networks
Next Steps:
Future plans include incorporating a variety of choices for monitor types for MultiConn implementation, such as seeing edges between neighbors and 2 or 3 step neighbors
Distribution type Type