BPSL: a new rumor source location algorithm based on the time-stamp back propagation in social networks

计算机科学 快照(计算机存储) 谣言 随机性 观察员(物理) 期望最大化算法 算法 数据挖掘 最大似然 数学 统计 政治学 公共关系 量子力学 操作系统 物理
作者
Liqing Qiu,Shiqi Sai,Moji Wei
出处
期刊:Applied Intelligence [Springer Science+Business Media]
卷期号:52 (8): 8603-8615 被引量:4
标识
DOI:10.1007/s10489-021-02919-w
摘要

Finding a rumor source is a major issue in the analysis of social networks. In this problem, the rumor source is usually estimated from a given diffusion snapshot. How to estimate the rumor source accurately is a challenging problem. Usually, the rumor source location problem is regarded as a node ranking problem. However, most of the existing algorithms ignore the structure of the infected subgraph or the randomness of the rumor spread. Therefore, they have defects in applicability and accuracy. To solve this problem, this paper takes into account the above two aspects at the same time, and propose a new algorithm to locate the rumor source, which is called Back Propagation Source Location(BPSL). The proposed algorithm contains an estimation method which is based on the time-stamp back propagation. This method makes the proposed algorithm’s accuracy outperform previous algorithms’ accuracy. Moreover, the susceptible-infected model is used to simulate the information spread of the networks. The steps of the proposed algorithm can be stated as follows. First, a new method based on the influence maximization is proposed to determine the observer set, which can greatly reduce the number of observer nodes. Second, a new estimation method based on the time-stamp back propagation is proposed to locate the source, which makes the proposed algorithm more accuracy and doesn’t change the structure of infected subgraph at the same time. Finally, the experimental results on two artificial networks and four real-world networks show the superiority of the proposed algorithm.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
欣慰妙海完成签到 ,获得积分20
刚刚
CodeCraft应助zhaopeipei采纳,获得10
刚刚
LIUYONG发布了新的文献求助10
1秒前
lin发布了新的文献求助10
3秒前
4秒前
九湖夷上完成签到 ,获得积分10
4秒前
噼里啪啦完成签到 ,获得积分10
5秒前
大个应助hahaha123213123采纳,获得30
5秒前
5秒前
惊天大幂幂完成签到,获得积分10
5秒前
英姑应助Fang Xianxin采纳,获得10
6秒前
宋老师发布了新的文献求助30
6秒前
王洋完成签到,获得积分10
7秒前
lw777完成签到,获得积分20
7秒前
慢慢完成签到,获得积分10
7秒前
8秒前
靖123456发布了新的文献求助10
8秒前
拓跋箴完成签到,获得积分10
8秒前
彭于晏应助zy采纳,获得10
9秒前
精明玲完成签到 ,获得积分10
10秒前
10秒前
乐乐完成签到,获得积分10
11秒前
VirSnorlax完成签到,获得积分10
11秒前
SciGPT应助LL采纳,获得10
11秒前
妖孽宇发布了新的文献求助10
12秒前
aa完成签到,获得积分20
12秒前
aaaa完成签到,获得积分10
12秒前
马香芦完成签到,获得积分10
13秒前
西红柿完成签到,获得积分10
14秒前
15秒前
懵懂的冬灵完成签到,获得积分10
15秒前
碧蓝可仁完成签到 ,获得积分10
16秒前
王拉拉完成签到 ,获得积分10
16秒前
西西完成签到,获得积分10
16秒前
深情安青应助mkmimii采纳,获得10
17秒前
上官若男应助小王采纳,获得10
17秒前
bkagyin应助旦皋采纳,获得10
17秒前
Orange应助欣欣采纳,获得10
18秒前
玄学大哥完成签到,获得积分10
18秒前
18秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
Research on Disturbance Rejection Control Algorithm for Aerial Operation Robots 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038388
求助须知:如何正确求助?哪些是违规求助? 3576106
关于积分的说明 11374447
捐赠科研通 3305798
什么是DOI,文献DOI怎么找? 1819322
邀请新用户注册赠送积分活动 892672
科研通“疑难数据库(出版商)”最低求助积分说明 815029