Accession Number : ADA621982


Title :   Analyzing Evolving Social Network 2 (EVOLVE2)


Descriptive Note : Final rept. Jun 2012-Oct 2014


Corporate Author : UNIVERSITY OF SOUTHERN CALIFORNIA LOS ANGELES


Personal Author(s) : Lerman, Kristina


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a621982.pdf


Report Date : Apr 2015


Pagination or Media Count : 24


Abstract : Current social network analytic methods analyze a static aggregate graph, which provides a limited view of the structure and behavior of real world social networks. Real world networks are dynamic: they evolve over time as new connections form between individuals, and networks themselves act as a substrate for the flow of information and influence. Ignoring dynamics can produce a distorted, and even wrong, view of who the important individuals are in a social network, what is the nature and strength of the connections between them, and what are the communities of similar or similarly behaving individuals. The erroneous conclusion reached by static network analysis will waste analysts' time and resources. For these reasons, we developed network analysis methods that directly incorporate time. The research had two major threads: -Understand how networks evolve over time, and how changes in topology affect evolution of influence and groups -Understand the impact of dynamics and network flows on the measurement of the network structure.


Descriptors :   *NETWORK TOPOLOGY , *ONLINE COMMUNITIES , BEHAVIOR , COMMUNITY RELATIONS , DATA MINING , DYNAMICS , EVOLUTION(GENERAL) , GRAPHS , HEURISTIC METHODS , INFORMATION EXCHANGE , INFORMATION RETRIEVAL , INTERACTIONS , KNOWLEDGE MANAGEMENT , MATHEMATICAL PREDICTION , MATRICES(MATHEMATICS) , NETWORK ANALYSIS(MANAGEMENT) , NETWORK FLOWS , NODES , SOCIAL COMMUNICATION , VECTOR ANALYSIS


Subject Categories : Information Science
      Sociology and Law
      Computer Systems


Distribution Statement : APPROVED FOR PUBLIC RELEASE