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基于用户与节点规模的微博突发话题传播预测算法

王巍1,李锐光2,周渊2,杨武1   

  1. 1. 哈尔滨工程大学 信息安全研究中心,黑龙江 哈尔滨 150001; 2. 国家计算机网络应急技术处理协调中心,北京 100029
  • 出版日期:2013-08-25 发布日期:2013-12-16
  • 基金资助:
    国家自然科学基金资助项目(61170242, 61272536);国家高技术研究发展计(“863”计划)基金资助项目(2012AA012802);中央高校基本科研业务费专项基金资助项目(HEUCF100601)

Microblog burst topic diffusion prediction algorithmbased on the users and node scale

  • Online:2013-08-25 Published:2013-12-16

摘要: 突发话题传播建模与预测的主要目的是对网络中可能产生不良影响的、紧急性突发事件的后续传播进行控制。目前微博网络中的话题传播与预测研究尚处于起步阶段。通过对病毒传染模型、消息传播模型以及话题传播模型的深入研究,提出一种基于微博粉丝关系、用户活跃度和影响力的话题传播模型,将微博用户集合划分为感染用户、易染用户和免疫用户,分析感染用户和易染用户的粉丝关系,预测下个时间窗口内被感染的用户规模。沿用话题传播模型研究中的“内外场强”概念,通过研究发现“内场强”和“外场强”有特定的比例关系,基于用户群的规模大小,分别提出基于用户和节点规模的话题传播预测算法。相关实验表明,基于用户的算法预测更为准确但是时间复杂度较高,基于节点规模的算法则更适合大规模数据集的处理。

Abstract: The main purpose of burst topic diffusion modeling and prediction is to control the subsequent large-scale dissemination of emergency incidents with adverse effect. Currently microblog topic diffusion and prediction is still in its infancy. The viral infection model, the message propagation model and topic propagation model were deeply studied and a topic diffusion model was proposed based on fans relationship, user activity and influence. By partitioning microblog users into infected users, tangible user and immune user, the relationship between infected and tangible user was analyzed to predict the scale of users which were infected in next time window. Following "internal and external field strength" concept in topic diffusion model, the proportional relationship between them was studied. Based on the scale of the user, topic diffusion prediction algorithms were proposed based on user and node scale respectively. Experiments show that the former can predict diffusion more accurately but with bad time complexity, and the latter node is more suitable for processing large data sets.

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