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- 2016
综合用户相似性与话题时效性的影响力用户发现算法
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Abstract:
摘要： 社交网络服务每天产生大量涉及众多话题的信息,并在影响力各异的用户群体推动下广泛传播。在IP(influence passivity)算法的基础上,提出了一种综合话题相似性与信息时效性的影响力用户发现算法EIP(extended influence-passivity)。该算法在转发网络上考虑用户间话题的相似性以及博文信息时效性,更加精准地建模和计算用户的影响力和消极性。基于新浪微博上爬取的约10万用户数据集上的实验验证,EIP影响力度量算法优于IP和TwitterRank等现有方法。
Abstract: Enormous information flowing through Online Social Media nowadays, spreading through hundreds of millions of users with different influence in the network. EIP(extended influence-passivity), an extension of IP(influence passivity)algorithm, is proposed to identify influencers in social network based on users forwarding activity. EIP measures the influence and passivity of users taking both pair wise topical similarity and timeliness feature of information into account. An evaluation performed with about 100 000 user dataset crawled from Sina micro-blog shows that EIP outperforms than other algorithms, including the original IP and TwitterRank
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