Maximizing social influence on target users

Yu Ting Wen*, Wen-Chih Peng, Hong-Han Shuai

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations


Influence maximization has attracted a considerable amount of research work due to the explosive growth in online social networks. Existing studies of influence maximization on social networks aim at deriving a set of users (referred to as seed users) in a social network to maximize the expected number of users influenced by those seed users. However, in some scenarios, such as election campaigns and target audience marketing, the requirement of the influence maximization is to influence a set of specific users. This set of users is defined as the target set of users. In this paper, given a target set of users, we study the Target Influence Maximization (TIM) problem with the purpose of maximizing the number of users within the target set. We particularly focus on two important issues: (1) how to capture the social influence among users, and (2) how to develop an efficient scheme that offers wide influence spread on specified subsets. Experiment results on real-world datasets validate the performance of the solution for TIM using our proposed approaches.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings
EditorsGeoffrey I. Webb, Dinh Phung, Mohadeseh Ganji, Lida Rashidi, Vincent S. Tseng, Bao Ho
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783319930398
StatePublished - 1 Jan 2018
Event22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018 - Melbourne, Australia
Duration: 3 Jun 20186 Jun 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10939 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018

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