Influence Maximization on Social Graphs: A Survey

By Yuchen Li, Ju Fan, Yanhao Wang, Kian-Lee Tan. IEEE Transactions on Knowledge and Data Engineering, 2018

618 citations recorded by the source corpus.

Influence Maximization (IM), which selects a set of <inline-formula><tex-math notation="LaTeX">$k$</tex-math> <alternatives><inline-graphic xlink:href="li-ieq1-2807843.gif"/></alternatives></inline-formula> 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 <italic>algorithmic perspective</italic>, 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.

Subfields: Statistical and Nonlinear Physics, Transportation

View Paper: https://www.semanticscholar.org/paper/de5e4c789209dfed61d427675e20ccf7362b9375