Sequential seeding to optimize influence diffusion in a social network

被引：16
|
作者：
Ni, Yaodong [1 ]
机构：
[1] Univ Int Business & Econ, Sch Informat Technol & Management, Beijing 100029, Peoples R China
来源：
APPLIED SOFT COMPUTING | 2017年 / 56卷
基金：
中国国家自然科学基金;
关键词：
Social networks; Complete influence time; Markov decision process; Online algorithm; Modified greedy algorithm; COMPLETE INFLUENCE TIME; INFLUENCE MAXIMIZATION; MODEL;
D O I：
10.1016/j.asoc.2016.04.025
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The problem of node seeding for optimizing influence diffusion in a social network can be applied in many fields, and thus has drawn much attention. In real life, because of a variety of reasons, decision maker needs to make a sequence of decisions about how to select the seeded nodes. In this paper, we study the problem of sequentially seeding nodes in a social network such that the complete influence time is minimized. We formulate a Markov decision process to describe the problem and embed a modified greedy search method into an online algorithm to solve the Markov decision process. Numerical experiments are performed to show the effectiveness of the proposed online algorithm. (C) 2016 Elsevier B.V. All rights
引用
收藏
页码：730 / 737
页数：8
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