TY - JOUR
T1 - CIM
T2 - Community-based influence maximization in social networks
AU - Chen, Yi Cheng
AU - Zhu, Wen Yuan
AU - Peng, Wen Chih
AU - Lee, Wang Chien
AU - Lee, Suh Yin
PY - 2014/4
Y1 - 2014/4
N2 - Given a social graph, the problem of influence maximization is to determine a set of nodes that maximizes the spread of influences. While some recent research has studied the problem of influence maximization, these works are generally too time consuming for practical use in a large-scale social network. In this article, we develop a new framework, community-based influence maximization (CIM), to tackle the influence maximization problem with an emphasis on the time efficiency issue. Our proposed framework, CIM, comprises three phases: (i) community detection, (ii) candidate generation, and (iii) seed selection. Specifically, phase (i) discovers the community structure of the network; phase (ii) uses the information of communities to narrow down the possible seed candidates; and phase (iii) finalizes the seed nodes from the candidate set. By exploiting the properties of the community structures, we are able to avoid overlapped information and thus efficiently select the number of seeds to maximize information spreads. The experimental results on both synthetic and real datasets show that the proposed CIM algorithm significantly outperforms the state-of-The-art algorithms in terms of efficiency and scalability, with almost no compromise of effectiveness.
AB - Given a social graph, the problem of influence maximization is to determine a set of nodes that maximizes the spread of influences. While some recent research has studied the problem of influence maximization, these works are generally too time consuming for practical use in a large-scale social network. In this article, we develop a new framework, community-based influence maximization (CIM), to tackle the influence maximization problem with an emphasis on the time efficiency issue. Our proposed framework, CIM, comprises three phases: (i) community detection, (ii) candidate generation, and (iii) seed selection. Specifically, phase (i) discovers the community structure of the network; phase (ii) uses the information of communities to narrow down the possible seed candidates; and phase (iii) finalizes the seed nodes from the candidate set. By exploiting the properties of the community structures, we are able to avoid overlapped information and thus efficiently select the number of seeds to maximize information spreads. The experimental results on both synthetic and real datasets show that the proposed CIM algorithm significantly outperforms the state-of-The-art algorithms in terms of efficiency and scalability, with almost no compromise of effectiveness.
UR - http://www.scopus.com/inward/record.url?scp=84899737385&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84899737385&partnerID=8YFLogxK
U2 - 10.1145/2532549
DO - 10.1145/2532549
M3 - Article
AN - SCOPUS:84899737385
VL - 5
JO - ACM Transactions on Intelligent Systems and Technology
JF - ACM Transactions on Intelligent Systems and Technology
SN - 2157-6904
IS - 2
M1 - 25
ER -