[Purpose/significance] Scientific research communities are important knowledge groups in contemporary science. Studying the evolutionary characteristics of scientific research communities is of great significance for exploring the law of field development and promoting knowledge innovation.[Method/process] This article took the field of Library and Information Science (LIS) as an example. From the perspective of evolutionary event detection, this paper used the Leiden algorithm to detect scientific research communities, and constructed their evolution paths and evolution trees. On this basis, this paper identified the evolution events of scientific research communities, and revealed the evolution modes and evolution characteristics of scientific research communities from three aspects:the overall analysis of the evolution, the evolution paths and the characteristics of the evolution trees, and the statistical characteristics of group evolution events.[Result/conclusion] The research shows that the scale of scientific research communities is developing vigorously, and the evolution trees of scientific research communities present two evolution modes. Most of the evolutionary events of growth type occurred in large communities with a relatively high volume of posts, while both ‘form’ and ‘dissovle’ evolution events occurred in small communities with a relatively high volume of posts. The average community size of evolutionary events such as ‘merge’, ‘partial merge’, ‘split’, and ‘shrink’ is small, and the volume of publications is low. These characteristics further prove that the cooperation and exchanges between scientific research communities tend to be frequent, and the evolution of scientific research communities has become increasingly complex.
Li Gang
,
Tang Jing
,
Mao Jin
,
Tian Yunpei
,
Zhang Bin
. Research on the Evolution Characteristics of Scientific Research Communities in Subject Fields Based on Evolutionary Event Detection-An Example of LIS[J]. Library and Information Service, 2021
, 65(17)
: 79
-90
.
DOI: 10.13266/j.issn.0252-3116.2021.17.008
[1] 任妮, 周建农.合著网络加权模式下科研团队的发现与评价研究[J]. 现代图书情报技术, 2015(9):68-75.
[2] EVANS T S, LAMBIOTTE R, PANZARASA P. Community structure and patterns of scientific collaboration in business and management[J]. Scientometrics, 2011, 89(1):381-396.
[3] MAO J, CAO Y, LU K, et al. Topic scientific community in science:a combined perspective of scientific collaboration and topics[J]. Scientometrics, 2017, 112(4):851-875.
[4] LIU X, BOLLEN J, NELSON M L, et al. Co-authorship networks in the digital library research community[J]. Information processing & management, 2005, 41(6):1462-1480.
[5] 李亮, 朱庆华.社会网络分析方法在合著分析中的实证研究[J]. 情报科学, 2008, 26(4):549-555.
[6] MASMOUDI A, MEZGHANI E, BELLAAJ H, et al. A web-based knowledge management system for scientific research team[C]//2017 IEEE 26th international conference on enabling technologies:infrastructure for collaborative enterprises. Poland:IEEE, 2017.
[7] 陈文杰.融合节点主题特征的社团发现研究[J/OL]. 情报理论与实践:1-10[2021-01-22]. http://kns.cnki.net/kcms/detail/11.1762.g3.20201223.0841.002.html.
[8] JUNG S, YOON W C. An alternative topic model based on common interest authors for topic evolution analysis[J]. Journal of informetrics, 2020, 14(3):101040.
[9] ROSVALL M, BERGSTROM C T. Maps of random walks on complex networks reveal community structure[J]. Proceedings of the national academy of sciences, 2008, 105(4):1118-1123.
[10] RAGHAVAN U N, RÉKA ALBERT, KUMARA S. Near linear time algorithm to detect community structures in large-scale networks[J]. Physical review E, 2007, 76(3 Pt 2):036106.
[11] BLONDEL V D, GUILLAUME J L, LAMBIOTTE R, et al. Fast unfolding of communities in large networks[J]. Journal of statistical mechanics theory & experiment, 2008, 10:P10008.
[12] 孙扬. 社交网络中社区演化事件检测研究[D]. 上海:上海交通大学, 2016.
[13] 余厚强, 白宽, 邹本涛, 等.人工智能领域科研团队识别与领军团队提取[J]. 图书情报工作, 2020, 64(20):4-13.
[14] 邹本涛, 王曰芬, 余厚强.人工智能领域高产科研团队的演化研究[J]. 图书情报工作, 2020, 64(20):23-33.
[15] 江文华, 徐健, 李纲, 等.基于研究兴趣相似性网络的我国图书馆学研究社群分析[J]. 现代情报, 2019, 39(9):21-27.
[16] TRAAG V, WALTMAN L, VAN ECK N J. From louvain to leiden:guaranteeing well-connected communities[J]. Scientific reports, 2019, 9(1):5233
[17] 韩童茜, 王立梅, 许鑫.长三角城市群科研合作网络演化研究——基于SCIE和SSCI论文的实证分析[J]. 情报理论与实践, 2020, 43(10):151-156.
[18] 李纲, 李春雅, 李翔.基于社会网络分析的科研团队发现研究[J]. 图书情报工作, 2014, 58(7):63-70, 82.
[19] 王曰芬, 李冬琼, 余厚强.生命周期阶段中的科学合作网络演化及高影响力学者成长特征研究[J]. 情报学报, 2018, 37(2):121-131.
[20] ASUR S, PARTHASARATHY S, UCAR D. An event-based framework for characterizing the evolutionary behavior of interaction graphs[J]. ACM transactions on knowledge discovery from data, 2009, 3(4):1-36.
[21] TAKAFFOLI M, FAGNAN J, SANGI F, et al. Tracking changes in dynamic information networks[C]//International conference on computational aspects of social networks. Spain:IEEE, 2011.
[22] PALLA G, BARABÁSI, ALBERT-LÁSZLÓ, VICSEK, TAMÁS. Quantifying social group evolution[J]. Nature, 2007(446):664-667.
[23] MOHAMMADMOSAFERI K K, NADERI H. Evolution of communities in dynamic social networks:an efficient map-based approach[J]. Expert systems with applications, 2020(147):113221.
[24] 汤强. 科研网络的社区发现及演化特征研究[D]. 西安:西安电子科技大学, 2015.
[25] BAE S H, D HALPERIN, WEST J D, et al. Scalable and efficient flow-based community detection for large-scale graph analysis[J]. ACM transactions on knowledge discovery from data, 2017, 11(3):1-30.
[26] 胡昌平, 陈果.层次视角下概念知识网络的三元关系形态研究[J]. 图书情报工作, 2014, 58(4):11-16.
[27] SHANNON, P. Cytoscape:a software environment for integrated models of biomolecular interaction networks[J]. Genome research, 2003, 13(11):2498-2504.
[28] 徐兵, 赵亚伟, 徐杨远翔.基于关联群演化相似度的社团追踪算法[J]. 复杂系统与复杂性科学, 2019, 16(1):14-25.