Topic modeling methods have usually been applied in the past to identify the research interests of researchers. Observing the scientific growth, the trending topics can be identified as Stable, Hot, or Cold. Finding rising stars (junior researchers, who are at the start of their career) from a bibliometric network is a challenging task, specifically if the researchers have an interest in multiple sub-domains or are working on diverse topics. Existing methods for finding rising stars explore the co-author networks or citation networks, and ignore the textual content, which may help in finding rising stars through hot topics detection over time. A publication contributing to a hot topic can be an indication that the author of that publication may be a rising star and can become an expert in that domain in the future. This study proposes the Hot Topics Rising Star Rank (HTRS-Rank) method for finding rising stars by detecting hot topics. HTRS-Rank finds the junior scholars, who contribute to hot topics at the start of their career and ranks them based on the presence of hot topics in their publications. AMiner five years dataset ranging from 2005–2009 is selected for experimentation. Top 10 researchers are considered to measure the association strength using rank correlation among HTRS-Rank and baseline methods. Experimental results show the efficiency of HTRS-Rank in comparison to the baseline methods. The proposed HTRS Rank (TF–IDF) provides low standard deviation for productivity, citations and sociality as compared to baseline methods for more social and highly cited authors. It is identified that HTRS-Rank (WordNet) emphasizes the semantic similarity of two sentences, whereas HTRS-Rank (TF–IDF) scheme emphasizes the uniqueness or importance of each term, therefore TF–IDF approach performs better than WordNet approach due to having higher correlation with StarRank and WMIRank.

中文翻译：

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通过热点话题检测寻找后起之秀

过去通常应用主题建模方法来确定研究人员的研究兴趣。观察科学的发展，趋势主题可以分为稳定、热门或冷门。从文献计量网络中寻找后起之秀（处于职业生涯初期的初级研究人员）是一项具有挑战性的任务，特别是如果研究人员对多个子领域感兴趣或正在研究不同的主题。现有的寻找后起之秀的方法探索合著者网络或引文网络，而忽略文本内容，这可能有助于通过随着时间的推移检测热点主题来寻找后起之秀。对热门话题做出贡献的出版物可能表明该出版物的作者可能是后起之秀，并且将来可能成为该领域的专家。本研究提出了热门话题新星排名（HTRS-Rank）方法，通过检测热门话题来寻找新星。 HTRS-Rank 寻找在职业生涯初期对热点话题做出贡献的初级学者，并根据其出版物中热点话题的出现情况对他们进行排名。选择2005-2009年的AMiner五年数据集进行实验。排名前 10 位的研究人员被认为使用 HTRS-Rank 和基线方法之间的排名相关性来衡量关联强度。实验结果显示了 HTRS-Rank 与基线方法相比的效率。与针对更具社交性和高被引作者的基线方法相比，拟议的 HTRS 排名 (TF-IDF) 为生产力、引用和社交性提供了较低的标准差。结果表明，HTRS-Rank (WordNet) 强调两个句子的语义相似性，而 HTRS-Rank (TF–IDF) 方案则强调每个术语的唯一性或重要性，因此 TF–IDF 方法比 WordNet 方法表现更好，因为与 StarRank 和 WMIRank 的相关性较高。