Influence Maximization on Social Graphs: A Survey

Influence Maximization (IM), which selects a set of k users (called seed set) from a social network to maximize the expected number of influenced users (called influence spread), is a key algorithmic problem in social influence analysis. Due to its immense application potential and enormous technica...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2018-10, Vol.30 (10), p.1852-1872
Main Authors: Li, Yuchen, Fan, Ju, Wang, Yanhao, Tan, Kian-Lee
Format: Article
Language:English
Subjects:
algorithm design
Algorithms
Classification algorithms
Computational modeling
Diffusion processes
Influence maximization
information diffusion
Information dissemination
Integrated circuit modeling
Maximization
Social network services
Social networks
Stochastic processes
Taxonomy
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Influence Maximization (IM), which selects a set of k users (called seed set) from a social network to maximize the expected number of influenced users (called influence spread), is a key algorithmic problem in social influence analysis. Due to its immense application potential and enormous technical challenges, IM has been extensively studied in the past decade. In this paper, we survey and synthesize a wide spectrum of existing studies on IM from an algorithmic perspective, with a special focus on the following key aspects: (1) a review of well-accepted diffusion models that capture the information diffusion process and build the foundation of the IM problem, (2) a fine-grained taxonomy to classify existing IM algorithms based on their design objectives, (3) a rigorous theoretical comparison of existing IM algorithms, and (4) a comprehensive study on the applications of IM techniques in combining with novel context features of social networks such as topic, location, and time. Based on this analysis, we then outline the key challenges and research directions to expand the boundary of IM research.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2018.2807843