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基于LSTM模型与加权链路预测的学科新兴主题成长性识别研究

来源:用户上传      作者:段庆锋 陈红 刘东霞 闫绪娴 张红兵

  摘要:[目的/意义]揭示学科主题潜在的成长性趋势是识别其新兴特征的关键和难点。[方法/过程]从热度和影响力两个维度考察主题成长性,通过基于机器学习算法的预测模型估计主题潜在成长性,基于此形成新兴主题的细分类型划分。首先,设计融合文献计量指标和替代计量指标的主题热度指标,构建基于长短记忆神经网络LSTM的主题热度预测模型,预测主题热度增长率;其次,基于加权链路预测相似性指标,构建旨在预测未来主题网络的三层神经网络预测模型,并采用PageRank算法得到主题影响力增长率预测值;最后,基于热度增长率预测值和影响力增长率预测值构建二维识别空间,通过主题聚类,识别不同子类型的学科新兴主题。[结果/结论]以情报学为领域的实证研究检验了识别方法的有效性,反映了成长性预测指标对于新兴特征的敏感捕捉能力。
  关键词:学科新兴主题;成长性;识别;加权链路预测;LSTM
  DOI:10.3969/j.issn.1008-0821.2022.09.004
  〔中图分类号〕G254〔文献标识码〕A〔文章编号〕1008-0821(2022)09-0037-12
  Identifying Growth of Discipline Topics Using
  LSTM and Weighted Link PredictionDuan QingfengChen HongLiu DongxiaYan XuxianZhang Hongbing
  (School of Management,Shanxi University of Finance & Economics,Taiyuan 030006,China)
  Abstract:[Purposes/Significance]The key and difficult point during the period when identifying emergent characteristics of topic in discipline is to disclose growing trend in potentially.[Method/Process]The topic's growth was investigated from two aspects of hotness and influence,and the extent to which topics grow up was estimated by using machine-learning algorithm as predicting model.Based on this,emerging topics were classified into different sub-categories.Firstly,hotness index that combines bibliometric indicator and altmetric indicator was designed,and then a prediction model that use LSTM to predict the extent to which hotness index increase was established.Secondly,another prediction model that has three-layer neural network and can predict newly occurred link in future between two topics based on the similarity index from weighted link prediction was proposed.Based on those,PageRank algorithm was used to estimate influence of a topic embedded in the network we had predicted.Finally,a comprehensive method was offered to discern different types of emergent topic.The method constructed a two-dimensional recognition space,using the growth indicators including hotness and influence,to conduct clustering analysis on topics.[Result/Conclusion]The paper conducted an empirical study with the samples from the discipline of information science,which successfully confirmed the effectiveness of our proposed method.Results illustrated that those index for growth prediction are sensitive enough to emergency.
  Key words:emerging topics in discipline;growth;identification;weighted link prediction;LSTM
  新d主题已经成为科技情报学界持续关注的热点和难点。代表科技趋势的学科新兴主题能够为国家科技战略规划、企业增强科技竞争力、研发人员寻找技术机会提供关键的决策依据,具有极高的战略价值。成长性是新兴主题的重要表现,更是识别新兴主题的关键。通过梳理相关文献可以发现,尽管新兴主题识别采用的逻辑依据各有不同,但出现最多的就是成长性(Growth)特征[1]。通过捕捉成长性特征发现新兴主题识别领域的基本共识。然而,成长性具有鲜明的动态属性,学科趋势面临诸多不确定性,预测甚至洞见学科未来存在挑战性。

nlc202209161503



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