Real-time topic-aware influence maximization using preprocessing.

Q1 Mathematics

Computational Social Networks Pub Date : 2016-01-01 Epub Date: 2016-11-10 DOI:10.1186/s40649-016-0033-z

Wei Chen, Tian Lin, Cheng Yang

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引用次数: 41

Abstract

Background: Influence maximization is the task of finding a set of seed nodes in a social network such that the influence spread of these seed nodes based on certain influence diffusion model is maximized. Topic-aware influence diffusion models have been recently proposed to address the issue that influence between a pair of users are often topic-dependent and information, ideas, innovations etc. being propagated in networks are typically mixtures of topics.

Methods: In this paper, we focus on the topic-aware influence maximization task. In particular, we study preprocessing methods to avoid redoing influence maximization for each mixture from scratch.

Results: We explore two preprocessing algorithms with theoretical justifications.

Conclusions: Our empirical results on data obtained in a couple of existing studies demonstrate that one of our algorithms stands out as a strong candidate providing microsecond online response time and competitive influence spread, with reasonable preprocessing effort.

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使用预处理实现实时主题感知影响最大化。

背景:影响最大化是在社会网络中找到一组种子节点，使这些种子节点的影响传播基于一定的影响扩散模型最大化的任务。最近提出了主题感知影响扩散模型，以解决一对用户之间的影响往往依赖于主题的问题，而在网络中传播的信息、想法、创新等通常是主题的混合物。方法:本文主要研究话题感知影响最大化任务。特别是，我们研究了预处理方法，以避免从头开始对每个混合物进行重做影响最大化。结果:我们探索了两种预处理算法，并给出了理论依据。结论:我们对现有研究中获得的数据的实证结果表明，我们的一种算法在合理的预处理工作下，能够提供微秒级的在线响应时间和竞争影响力传播，是一种强有力的候选算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computational Social Networks Mathematics-Modeling and Simulation

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0.00%

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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.