Approaches of influence maximization in  social networks with positive and negative  opinions

Authors

Jiaguo Lv School of Information Science and Engineering, Zaozhuang University, Zaozhuang, Shandong, 277100, China Author
Jingfeng Guo The Key Laboratory for Computer Virtual Technology and System Integration Hebei Province, Qinhuangdao, Hebei, 066004, China Author
Yuanying Liu School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei, 066000, China Author
Allen Jocshi Network Information Center for Design and Analysis, MCCN Ltd,11952,Gdansk, Poland Translator

Viral marketing, Influence maximization, Social network, Negative opinions, LTN model

In viral marketing, considering the phenomenon that nega

tive opinions may emerge and propagate in social networks,

based on the fundamental linear threshold model (LT), a new

model – linear threshold model with negative opinions (LTN)

was proposed in this study. Subsequently, some properties of

the LTN model, such as monotonicity and submodularity have

been shown. With these properties, a greedy approximate al

gorithm with a ratio of (1-1/e) for influence maximization on

the LTN model was proposed. To overcome the inefficiency of

the greedy algorithm, three improved algorithms—LTN_New

Greedy (NewGreedy algorithm on LTN), LTN_CELF(CELF

algorithm on LTN) and LTN_MixedGreedy (MixedGreedy

algorithm on LTN) have been provided in this work. The ex

perimental results on two synthetic datasets showed that the

influence spread of these improved algorithms was close to

that of those benchmark algorithms, but they were faster than

those benchmark algorithms.