基于引文网络社区发现的数据推荐研究

doi:10.3772/j.issn.1000-0135.2021.08.008

情报学报

2021, Vol. 40

Issue (8): 879-886 DOI: 10.3772/j.issn.1000-0135.2021.08.008

情报分析方法与技术

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基于引文网络社区发现的数据推荐研究

李成赞^1,2, 黎建辉¹, 王学志¹, 沈志宏¹, 杜一^1,2

1.中国科学院计算机网络信息中心，北京 100190
2.中国科学院大学，北京 100049

Research on Data Recommendation Based on Community Detection of Citation Network

Li Chengzan^1,2, Li Jianhui¹, Wang Xuezhi¹, Shen Zhihong¹, Du Yi^1,2

1.Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190
2.University of Chinese Academy of Sciences, Beijing 100049

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摘要科学数据是科研活动的输入和产出，是科技创新的核心驱动要素。科学数据只有开放共享、广泛传播才能使其价值最大化，然而当前数据出版物的利用率和传播效率整体偏低。为了加速科学数据的传播和重用，提升科学数据的开放共享成效，本文提出了一种基于引文网络社区发现的数据推荐方法。该方法在构建“数据集-论文-作者”之间关联网络的基础上，利用Louvain算法，从合著、共引和耦合三种关联方式分别进行社区发现，然后结合TF-IDF（term frequency-inverse document frequency）及余弦相似度算法，计算数据集与学术论文的相似性，并以此构建数据集与论文所在社区之间的关联，并进行数据集推荐。实验结果证明，基于引文网络社区发现的数据推荐方法，能够有效地发现对数据集潜在感兴趣的论文或作者。同时，发现在数据推荐效果的贡献度和稳定性方面，基于耦合关系的社区发现表现最优，合著关系次之，而引用关系则受出版时间长短和被引次数的影响导致推荐效果差异较大。

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关键词 ：数据推荐, 引文网络, 社区发现, 相似性度量

收稿日期: 2019-05-16

基金资助:国家重点研发计划项目“科学大数据管理系统”（2016YFB1000600）。

作者简介: 李成赞，男，1982年生，硕士，高级工程师，主要研究方向为计算机应用技术与数据开放共享，E-mail：lichengzan@cnic.cn；黎建辉，男，1973年生，博士生导师，研究员，主要研究方向为大数据技术架构与应用；王学志，男，1979年生，硕士生导师，研究员，主要研究方向科学大数据挖掘分析技术与应用；沈志宏，男，1977年生，博士，高级工程师，主要研究方向为大数据与知识图谱；杜一，男，1988年生，博士，副研究员，主要研究方向为大数据与知识图；

引用本文:

李成赞, 黎建辉, 王学志, 沈志宏, 杜一. 基于引文网络社区发现的数据推荐研究[J]. 情报学报, 2021, 40(8): 879-886.
Li Chengzan, Li Jianhui, Wang Xuezhi, Shen Zhihong, Du Yi. Research on Data Recommendation Based on Community Detection of Citation Network. 情报学报, 2021, 40(8): 879-886.

链接本文:

https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2021.08.008 或 https://qbxb.istic.ac.cn/CN/Y2021/V40/I8/879

1 Reinsel D, Gantz J, Rydning J. Data age 2025: the evolution of data to life-critical[J]. Framingham: IDC Analyze the Future, 2017: 2-16.
2 黎建辉, 吴超, 李成赞, 等. 科学数据出版调查[DB/OL]. Science Data Bank. DOI: 10.11922/sciencedb.840.87.
3 Nugroho R P, Zuiderwijk A, Janssen M, et al. A comparison of national open data policies: lessons learned[J]. Transforming Government: People, Process and Policy, 2015, 9(3): 286-308.
4 完颜邓邓, 高峰. 澳大利亚高校图书馆研究数据管理服务的调查分析[J]. 图书与情报, 2015(3): 71-76.
5 Boas Hall M. Henry oldenburg: shaping the royal society[M]. Oxford: Oxford University Press, 2002.
6 Chen P, Redner S. Community structure of the physical review citation network[J]. Journal of Informetrics, 2010, 4(3): 278-290.
7 Marion L S, Garfield E, Hargens L L, et al. Social network analysis and citation network analysis: complementary approaches to the study of scientific communication. Sponsored by SIG MET[J]. Proceedings of the American Society for Information Science and Technology, 2003, 40(1): 486-487.
8 Watts D J, Strogatz S H. Collective dynamics of ‘small-world’ networks[J]. Nature, 1998, 393(6684): 440-442.
9 Barabási A L, Albert R. Emergence of scaling in random networks[J]. Science, 1999, 286(5439): 509-512.
10 Broido A D, Clauset A. Scale-free networks are rare[J]. Nature Communications, 2019, 10(1): 1017.
11 叶腾, 韩丽川, 邢春晓, 等. 基于复杂网络的虚拟社区创新知识传播机制研究[J]. 现代图书情报技术, 2016(7/8): 70-77.
12 黄俊铭, 沈华伟, 程学旗. 利用社交网络的影响力骨架探索信息传播[J]. 中文信息学报, 2016, 30(2): 74-82.
13 David-Barrett T. Network effects of demographic transition[J]. Scientific Reports, 2019, 9(1): 2361.
14 Jiang M H, An H Z, Gao X Y, et al. Factors driving global carbon emissions: a complex network perspective[J]. Resources, Conservation and Recycling, 2019, 146: 431-440.
15 Li L N, Goodchild M F, Xu B. Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr[J]. Cartography and Geographic Information Science, 2013, 40(2): 61-77.
16 Tsou M H, Yang J A, Lusher D, et al. Mapping social activities and concepts with social media (Twitter) and web search engines (Yahoo and Bing): a case study in 2012 US Presidential Election[J]. Cartography and Geographic Information Science, 2013, 40(4): 337-348.
17 王艳东, 付小康, 李萌萌. 一种基于共词网络的社交媒体数据主题挖掘方法[J]. 武汉大学学报?信息科学版, 2018, 43(12): 2287-2294.
18 Li G S, Cai Z P, Yin G S, et al. Differentially private recommendation system based on community detection in social network applications[J]. Security and Communication Networks, 2018, 2018: 1-18.
19 Kaffash S, Marra M. Data envelopment analysis in financial services: a citations network analysis of banks, insurance companies and money market funds[J]. Annals of Operations Research, 2017, 253(1): 307-344.
20 Karunan K, Lathabai H H, Prabhakaran T. Discovering interdisciplinary interactions between two research fields using citation networks[J]. Scientometrics, 2017, 113(1): 335-367.
21 Lathabai H H, Prabhakaran T, Changat M. Centrality and flow vergence gradient based path analysis of scientific literature: a case study of biotechnology for engineering[J]. Physica A: Statistical Mechanics and its Applications, 2015, 429: 157-168.
22 Lathabai H H, Prabhakaran T, Changat M. Contextual productivity assessment of authors and journals: a network scientometric approach[J]. Scientometrics, 2017, 110(2): 711-737.
23 Leydesdorff L, Wagner C S, Bornmann L. Betweenness and diversity in journal citation networks as measures of interdisciplinarity: a tribute to eugene garfield[J]. Scientometrics, 2018, 114(2): 567-592.
24 Liu J S, Chen H H, Ho M H C, et al. Citations with different levels of relevancy: tracing the main paths of legal opinions[J]. Journal of the Association for Information Science and Technology, 2014, 65(12): 2479-2488.
25 Kessler M M. Bibliographic coupling between scientific papers[J]. American Documentation, 1963, 14(1): 10-25.
26 Small H. Co-citation in the scientific literature: a new measure of the relationship between two documents[J]. Journal of the American Society for Information Science, 1973, 24(4): 265-269.
27 Small H, Griffith B C. The structure of scientific literatures I: identifying and graphing specialties[J]. Science Studies, 1974, 4(1): 17-40.
28 肖雪, 陈云伟, 邓勇. 引文网络的社团划分研究进展综述[J]. 情报杂志, 2016, 35(4): 125-130.
29 Huang M H, Chang C P. A comparative study on detecting research fronts in the organic light-emitting diode (OLED) field using bibliographic coupling and co-citation[J]. Scientometrics, 2015, 102(3): 2041-2057.
30 Newman M E J. Coauthorship networks and patterns of scientific collaboration[J]. Proceedings of the National Academy of Sciences of the United States of America, 2004, 101(Suppl 1): 5200-5205.
31 韩青, 周晓英. 基于文献共被引特征的文献相似度计算优化研究[J]. 情报学报, 2018, 37(9): 905-911.
32 Zhao F, Zhang Y, Lu J G, et al. Measuring academic influence using heterogeneous author-citation networks[J]. Scientometrics, 2019, 118(3): 1119-1140.
33 Massucci F A, Docampo D. Measuring the academic reputation through citation networks via PageRank[J]. Journal of Informetrics, 2019, 13(1): 185-201.
34 Zhou J, Cai N, Tan Z Y, et al. Analysis of effects to journal impact factors based on citation networks generated via social computing[J]. IEEE Access, 2019, 7: 19775-19781.
35 West J D, Wesley-Smith I, Bergstrom C T. A recommendation system based on hierarchical clustering of an article-level citation network[J]. IEEE Transactions on Big Data, 2016, 2(2): 113-123.
36 Haruna K, Ismail M A, Bichi A B, et al. A citation-based recommender system for scholarly paper recommendation[C]// Proceedings of the International Conference on Computational Science and Its Applications. Cham: Springer, 2018: 514-525.
37 钱俊松, 冷文浩. 基于图数据库的TDM系统设计与实现[J]. 软件导刊, 2018, 17(9): 196-199.
38 Fortunato S, Hric D. Community detection in networks: a user guide[J]. Physics Reports, 2016, 659: 1-44.
39 Yang Z, Algesheimer R, Tessone C J. A comparative analysis of community detection algorithms on artificial networks[J]. Scientific Reports, 2016, 6: 30750.
40 Lancichinetti A, Fortunato S. Community detection algorithms: a comparative analysis[J]. Physical Review E, 2009, 80(5): 056117.
41 徐进, 邓乐龄. 基于Louvain算法的铁路旅客社会网络社区划分研究[J]. 山东农业大学学报(自然科学版), 2018, 49(4): 722-725.
42 陶跃华, 王锡钢, 王云爱. 信息检索向量空间模型中特征提取的研究[J]. 云南师范大学学报(自然科学版), 2000, 20(6): 18-20.
43 Tata S, Patel J M. Estimating the selectivity of TF-IDF based cosine similarity predicates[J]. ACM SIGMOD Record, 2007, 36(4): 75-80.
44 武永亮, 赵书良, 李长镜, 等. 基于TF-IDF和余弦相似度的文本分类方法[J]. 中文信息学报, 2017, 31(5): 138-145.