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 keyword Q and a time point t, 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 of Q at time (t + Δt); that is, excluding those detected at time t, topic experts obtained at time (t + Δt) can be regarded as rising stars at time t. 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 time t and 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.

中文翻译：

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社交网络中基于极限学习机的后起之秀评估

在社交网络中，冉冉升起的新星是初级人才，他们起初可能并不那么迷人，但随着时间的推移，他们变得非常出色。最近，后起之秀评估已成为社会分析领域的热门研究主题，对决策支持，认知计算和其他实际问题很有帮助。在本文中，我们研究了地缘社会网络中的后起之秀评估问题。具体来说，给定主题关键字Q和时间点t，我们旨在评估用户寻找后起之秀的潜在影响，这些后起之秀是指对基础地理社交网络目前活动很少且影响不大但将来可能会成为有影响力的专家的专家。为了有效地评估未来的恒星，我们提出了一种基于极限学习机（ELM）的新颖处理框架，称为FS-ELM。FS-ELM由三个关键组件组成。第一个组件通过合并社交拓扑和用户行为模式来构建功能。第二组分的提取物通过发现的主题专家监督信息Q在时间（吨+ Δ吨）; 也就是说，不包括在时间t检测到的那些，在时间t（t +Δ吨）可被视为分在时间上升吨。第三部分是基于ELM的未来之星分类，该分类利用ELM作为出发点来评估用户是否为后起之秀。我们对实际数据集进行的实验研究表明：（1）FS-ELM可以在时间t处以查询主题有效地发现升空星并优于其他传统方法；（2）用户的社会特征对后起之秀的评价产生重要影响。本文研究了一个新的问题，即地缘社会网络中的后起之秀评估。通过利用社交拓扑特征和用户行为模式，我们提出了一种基于ELM的高级处理框架。实验结果令人鼓舞地证明了所提出方法的效率和有效性。