Cascade source inference in networks: a Markov chain Monte Carlo approach.

Q1 Mathematics

Computational Social Networks Pub Date : 2015-01-01 Epub Date: 2015-10-19 DOI:10.1186/s40649-015-0017-4

Xuming Zhai, Weili Wu, Wen Xu

引用次数: 6

Abstract

Cascades of information, ideas, rumors, and viruses spread through networks. Sometimes, it is desirable to find the source of a cascade given a snapshot of it. In this paper, source inference problem is tackled under Independent Cascade (IC) model. First, the #P-completeness of source inference problem is proven. Then, a Markov chain Monte Carlo algorithm is proposed to find a solution. It is worth noting that our algorithm is designed to handle large networks. In addition, the algorithm does not rely on prior knowledge of when the cascade started. Finally, experiments on real social network are conducted to evaluate the performance. Under all experimental settings, our algorithm identified the true source with high probability.

Abstract Image

查看原文本刊更多论文

网络中的级联源推理:一种马尔可夫链蒙特卡罗方法。

信息、思想、谣言和病毒通过网络传播。有时，需要在给出级联快照的情况下找到它的源。本文研究了独立级联(Independent Cascade, IC)模型下的源推理问题。首先，证明了源推理问题的# p -完备性。然后，提出了一种马尔可夫链蒙特卡罗算法来求解。值得注意的是，我们的算法是为处理大型网络而设计的。此外，该算法不依赖于级联何时开始的先验知识。最后，在真实的社交网络上进行了实验来评估性能。在所有实验设置下，我们的算法都以高概率识别出真实的源。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Computational Social Networks Mathematics-Modeling and Simulation

自引率

0.00%

发文量

审稿时长

13 weeks

期刊介绍： Computational Social Networks showcases refereed papers dealing with all mathematical, computational and applied aspects of social computing. The objective of this journal is to advance and promote the theoretical foundation, mathematical aspects, and applications of social computing. Submissions are welcome which focus on common principles, algorithms and tools that govern network structures/topologies, network functionalities, security and privacy, network behaviors, information diffusions and influence, social recommendation systems which are applicable to all types of social networks and social media. Topics include (but are not limited to) the following: -Social network design and architecture -Mathematical modeling and analysis -Real-world complex networks -Information retrieval in social contexts, political analysts -Network structure analysis -Network dynamics optimization -Complex network robustness and vulnerability -Information diffusion models and analysis -Security and privacy -Searching in complex networks -Efficient algorithms -Network behaviors -Trust and reputation -Social Influence -Social Recommendation -Social media analysis -Big data analysis on online social networks This journal publishes rigorously refereed papers dealing with all mathematical, computational and applied aspects of social computing. The journal also includes reviews of appropriate books as special issues on hot topics.