Efficient Algorithms for Maximizing Group Influence in Social Networks  

作　　者：Peihuang Huang Longkun Guo Yuting Zhong 

机构地区：[1]College of Mathematics and Data Science,Minjiang University,Fuzhou 350108,China [2]College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350116,China

出　　处：《Tsinghua Science and Technology》2022年第5期832-842,共11页清华大学学报（自然科学版（英文版）

基　　金：supported by the Natural Science Foundation of Fujian Province (No. 2020J01845);the Educational Research Project for Young and MiddleAged Teachers of Fujian Provincial Department of Education (No. JAT190613);the National Natural Science Foundation of China (Nos. 61772005 and 92067108);the Outstanding Youth Innovation Team Project for Universities of Shandong Province (No. 2020KJN008)。

摘　　要：In social network applications,individual opinion is often influenced by groups,and most decisions usually reflect the majority’s opinions.This imposes the group influence maximization(GIM) problem that selects k initial nodes,where each node belongs to multiple groups for a given social network and each group has a weight,to maximize the weight of the eventually activated groups.The GIM problem is apparently NP-hard,given the NP-hardness of the influence maximization(IM) problem that does not consider groups.Focusing on activating groups rather than individuals,this paper proposes the complementary maximum coverage(CMC) algorithm,which greedily and iteratively removes the node with the approximate least group influence until at most k nodes remain.Although the evaluation of the current group influence against each node is only approximate,it nevertheless ensures the success of activating an approximate maximum number of groups.Moreover,we also propose the improved reverse influence sampling(IRIS) algorithm through fine-tuning of the renowned reverse influence sampling algorithm for GIM.Finally,we carry out experiments to evaluate CMC and IRIS,demonstrating that they both outperform the baseline algorithms respective of their average number of activated groups under the independent cascade(IC)model.

关 键 词：complementary maximum coverage(CMC) improved reverse influence sampling(IRIS) group influence maximization(GIM) independent cascade(IC)model 

分 类 号：O157.5[理学—数学]