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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. [ABSTRACT FROM AUTHOR]