DMCA
A Model For Predicting Influential Users In Social Network
BibTeX
@MISC{K_amodel,
author = {Sriganga B K and Ragini Krishna and Dr. Prashanth},
title = {A Model For Predicting Influential Users In Social Network},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Abstract — In the recent past and in the envisaged future of global business scenario, social networking platform such as: Face book, Twitter, Google plus +, LinkedIn, Orkut etc are visioned to get information, opinions, likes and dislikes, profile matching etc. These inputs are very essential parameters in order to design, devise and deliver many of the marketing and CRM strategies for corporates and organizations. In computational theory a social network can be represented in terms of defined structure either by tree or a graph. The network of nodes could be either static in nature or dynamic in nature as it evolves over period of time by adding or deleting nodes and edges. The study of influential members in a social network is an important research question in social network analysis. In order to find the most influential person, the most central node has to be identified. Centrality is the measure of most influential node, which is measured in terms of centrality metrics. There have been various definitions given by different researchers for centrality metric or variants of centrality metric, such as: degree centrality, closeness centrality, graph centrality, between-ness centrality, dynamic centrality, α-centrality, Eigen vector centrality, page rank, Katz Status score etc. It has been observed that most of the existing methods for measuring centrality metrics are suitable for static networks and the existing methods of computation of centrality either underestimate or overestimate centrality of some nodes. In this work concentration is laid on dynamic network in terms of dynamic centrality scores considering different values of tunable parameter. Then based on dynamic centrality score the most influential individual in a social network can be declared.
Keyphrases
social network influential user dynamic centrality centrality metric dynamic centrality score overestimate centrality page rank influential member degree centrality crm strategy different researcher important research question face book dynamic network graph centrality different value social network analysis global business scenario influential person katz status score etc tunable parameter recent past computational theory between-ness centrality envisaged future closeness centrality defined structure influential node essential parameter static network various definition eigen vector centrality central node work concentration influential individual
