TY - GEN
T1 - Labeled influence maximization in social networks for target marketing
AU - Li, Fa Hsien
AU - Li, Cheng Te
AU - Shan, Man Kwan
PY - 2011
Y1 - 2011
N2 - The influence maximization problem is to find a set of seed nodes which maximize the spread of influence in a social network. The seed nodes are used for the viral marketing to gain the maximum profits through the effective word-of-mouth. However, in more real-world cases, marketers usually target certain products at particular groups of customers. While original influence maximization problem considers no product information and target customers, in this paper, we focus on the target marketing. We propose the labeled influence maximization problem, which aims to find a set of seed nodes which can trigger the maximum spread of influence on the target customers in a labeled social network. We propose three algorithms to solve such labeled influence maximization problem. We first develop the algorithms based on the greedy methods of original influence maximization by considering the target customers. Moreover, we develop a novel algorithm, Maximum Coverage, whose central idea is to offline compute the pairwise proximities of nodes in the labeled social network and online find the set of seed nodes. This allows the marketers to plan and evaluate strategies online for advertised products. The experimental results on IMDb labeled social network show our methods can achieve promising performances on both effectiveness and efficiency.
AB - The influence maximization problem is to find a set of seed nodes which maximize the spread of influence in a social network. The seed nodes are used for the viral marketing to gain the maximum profits through the effective word-of-mouth. However, in more real-world cases, marketers usually target certain products at particular groups of customers. While original influence maximization problem considers no product information and target customers, in this paper, we focus on the target marketing. We propose the labeled influence maximization problem, which aims to find a set of seed nodes which can trigger the maximum spread of influence on the target customers in a labeled social network. We propose three algorithms to solve such labeled influence maximization problem. We first develop the algorithms based on the greedy methods of original influence maximization by considering the target customers. Moreover, we develop a novel algorithm, Maximum Coverage, whose central idea is to offline compute the pairwise proximities of nodes in the labeled social network and online find the set of seed nodes. This allows the marketers to plan and evaluate strategies online for advertised products. The experimental results on IMDb labeled social network show our methods can achieve promising performances on both effectiveness and efficiency.
UR - http://www.scopus.com/inward/record.url?scp=84862918552&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84862918552&partnerID=8YFLogxK
U2 - 10.1109/PASSAT/SocialCom.2011.152
DO - 10.1109/PASSAT/SocialCom.2011.152
M3 - Conference contribution
AN - SCOPUS:84862918552
SN - 9780769545783
T3 - Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011
SP - 560
EP - 563
BT - Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011
T2 - 2011 IEEE International Conference on Privacy, Security, Risk and Trust, PASSAT 2011 and 2011 IEEE International Conference on Social Computing, SocialCom 2011
Y2 - 9 October 2011 through 11 October 2011
ER -