Rising star evaluation based on extreme learning machine in geo-social networks
Rising star evaluation based on extreme learning machine in geo-social networks
复制标题
基于地理社交网络极限学习机的新星评价
DOI：
10.1007/s12559-019-09680-w
复制
发表时间：
2020
影响因子：
5.4
通讯作者：
Qing Zhongqing
中科院分区：
文献类型：
--
作者：
Ma Yuliang;Yuan Ye;Wang Guoren;Bi Xin;Qing Zhongqing
在社交网络中，后起之秀是指年轻人，他们起初可能并不那么迷人，但随着时间的推移，他们会变得出类拔萃。近年来，新兴星星评价已成为社会分析领域的一个热门研究课题，对决策支持、认知计算等实际问题有着重要的应用价值。本文研究了地理社会网络中的新星星星评价问题。具体来说，给定一个主题关键字Q和一个时间点t，我们的目标是评估潜在的影响力的用户，发现后起之秀，这是指专家谁没有多少活动，目前在底层的地理社会网络的影响力很小，但可能成为有影响力的专家在未来。为了有效地评估未来的明星，我们提出了一种新的处理框架的基础上极端学习机（ELM）称为FS-ELM。FS-ELM由三个关键组件组成。第一个组件通过整合社会拓扑和用户行为模式来构建功能。第二个组件通过在时间（t+Δt）发现Q的主题专家来提取监督信息;即，排除在时间（t+Δt）检测到的主题专家，在时间（t+Δt）获得的主题专家可以被视为在时间（t+Δt）的新星。第三个组件是基于ELM的未来星星分类，它利用ELM作为出发点来评估用户是否是一个冉冉升起的星星。我们在真实数据集上进行的实验研究表明：（1）FS-ELM可以有效地发现具有查询主题的明日之星，并且优于其他传统方法;（2）用户社会特征对明日之星星星评价有重要影响。本文研究了一个新的问题，即地理社会网络中的后起之秀星星评价。我们提出了一个先进的处理框架的基础上ELM利用社会拓扑特征和用户行为模式。实验结果充分证明了该方法的有效性。
In social networks, rising stars are junior individuals who may be not so charming at first but turn out to be outstanding over time. Recently, rising star evaluation has become a popular research topic in the field of social analysis, which is helpful for decision support, cognitive computation, and other practical problems. In this paper, we study the problem of rising star evaluation in geo-social networks. Specifically, given a topic keywordQand a time pointt, we aim at evaluating the latent influence of users to find rising stars, which refer to experts who have few activities and little impact currently on the underlying geo-social network but may become influential experts in the future. To efficiently evaluate future stars, we propose a novel processing framework based on extreme learning machine (ELM) called FS-ELM. FS-ELM consists of three key components. The first component constructs features by incorporating social topology and user behavior patterns. The second component extracts supervised information by discovering topic experts ofQat time (t+Δt); that is, excluding those detected at timet, topic experts obtained at time (t+Δt) can be regarded as rising stars at timet. The third component is ELM-based future star classification that leverages ELM as a departure point to evaluate whether a user is a rising star. Our experimental studies conducted on real-world datasets show that (1) FS-ELM can effectively discover rising stars with a query topic at timetand outperform other traditional methods and (2) user social characteristics have an important impact on the rising star evaluation. This paper studies a novel problem, namely, rising star evaluation in geo-social networks. We propose an advanced processing framework based on ELM by exploiting social topology characteristics and user behavior patterns. The experimental results encouragingly demonstrate the efficiency and effectiveness of the proposed approach.