Knowledge graph analysis of research hotspots and periodic evolution of regular graphs
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NETL
NSTL
万方数据
从Web of Science核心合集获取正则图研究全部期刊的文献数据,借助科学知识图谱绘制正则图研究2008-2022年的全时段高频关键词共现图谱,以及分时段高频关键词共现图谱(以每三年为一个时段).通过比较全时段和分时段高频关键词的周期演变,识别正则图的整体研究概况与热点,以及各周期的研究热点及变化规律.结果显示:(1)正则图2008-2022年的四大研究热点为:正则图的参数与相关性质、正则图的特殊图类、正则图的应用、正则图的代数刻画;(2)在正则图的热点研究主题中,强正则图、距离正则图、复杂网络与算法等各周期稳定且变化极少;(3)从2011年开始,正则图热点主题数量显著增多,出现了线性码、深度学习、机器学习等研究主题.
In this paper,the literature data of all journals in the field of regular graphs were obtained from the core collection of Web of Science,and the full-time high-frequency keyword co-occurrence maps and the time-divided high-frequency keyword co-occurrence maps in this field from 2008 to 2022 were drawn with the help of the mapping knowledge domains(every three years as a time period).By comparing the periodic evolution of high-frequency keywords in all periods and in different periods,the overall research situation and hot spots of regular graphs,as well as the research hot spots and changing rules in each period,were identified.The results show that:(1)The four research hotspots of regular graphs from 2008 to 2022 are parameters and related properties,special graph classes,applications and algebraic characterizations;(2)In the hot topics,research on strongly regular graphs,distance-regular graphs,complex networks and algorithms are stable in each period and rarely change;(3)Since 2011,the number of hot topics in the field of regular graphs have increased significantly,and topics such as linear coding,deep learning and machine learning have emerged.
NETL
NSTL
万方数据
