Paper Info
Title
Sales potential optimization on directed social networks: a quasi-parallel genetic algorithm approach
Abstract
New node centrality measurement for directed networks called the Sales Potential is introduced with the property that nodes with high Sales Potential have small in-degree and high second-shell in-degree. Such nodes are of great importance in online marketing strategies for sales agents and IT security in social networks. We propose an optimization problem that aims at finding a finite set of nodes, so that their collective Sales Potential is maximized. This problem can be efficiently solved with a Quasi-parallel Genetic Algorithm defined on a given topology of sub-populations. We find that the algorithm with a small number of sub-populations gives results with higher quality than one with a large number of sub-populations, though at the price of slower convergence.
Year
DOI
Venue
2012
10.1007/978-3-642-29178-4_12
Lecture Notes in Computer Science
Keywords
Field
DocType
high second-shell in-degree,sales potential optimization,high sales,finite set,it security,small number,quasi-parallel genetic algorithm,social network,small in-degree,large number,optimization problem,quasi-parallel genetic algorithm approach,collective sales,social networks
Convergence (routing),Small number,Mathematical optimization,Social network,Finite set,Computer science,Centrality,Online advertising,Optimization problem,Genetic algorithm
Conference
Citations 
PageRank 
References 
3
0.46
9
Authors
2
Name
Order
Citations
PageRank
Crown Guan Wang171.26
Kwok Yip Szeto26421.47