Paper Info
Title
Location-Aware Targeted Influence Blocking Maximization in Social Networks
Abstract
In this issue, we consider the location-aware targeted influence blocking maximization (LTIBM) problem, which plays a very important role in viral marketing and rumor control. LTIBM aims to find a set of positive seeds in a given social network to block the influence propagation of negative seeds over the targeted nodes located in a given region and having a preference on a given topic set as much as possible. We devise a simulation-based greedy algorithm based on monotone and submodular characteristics of influence function under the homogeneous independent cascade model. To improve the efficiency of the greedy algorithm, we propose LTIBM-H, a heuristic algorithm based on QT-tree and maximum influence arborescence (MIA). Experimental results show that the proposed LTIBM-H algorithm can achieve matching the blocking effect to the greedy algorithm and often performs better in terms of effectiveness than other baseline algorithms, while LTIBM-H is four orders of magnitude faster than the greedy algorithm.
Year
DOI
Venue
2019
10.1109/ICCCN.2019.8847090
2019 28th International Conference on Computer Communication and Networks (ICCCN)
Keywords
Field
DocType
social networks,location-aware targeted influence blocking maximization,viral marketing,rumor control,influence propagation,simulation-based greedy algorithm,maximum influence arborescence,LTIBM-H algorithm,blocking effect,LTIBM-H heuristic algorithm,QT-tree
Data modeling,Viral marketing,Mathematical optimization,Computer science,Heuristic (computer science),Submodular set function,Greedy algorithm,Arborescence,Monotone polygon,Maximization,Distributed computing
Conference
ISSN
ISBN
Citations 
1095-2055
978-1-7281-1857-4
0
PageRank 
References 
Authors
0.34
15
6
Name
Order
Citations
PageRank
Wenlong Zhu100.34
Yang Wu26922.62
Shichang Xuan321.40
Dapeng Man42910.54
Wei Wang57122746.33
Jiguang Lv600.68