对比数据挖掘与软件工程领域作者合作网络差异
Examining Different Research Communities: Authorship Network
- 基于2000-2021年谷歌学术数据构建双领域作者合作图谱
- 发现两领域网络结构迥异，均存在由核心作者构成的小型社区
- 适合关注科研合作模式或领域演化趋势的研究者阅读
谷歌学术是跨学科获取学术文献的重要搜索工具，其高级搜索功能支持按关键词、出版商、作者名、时间范围等提取文献。本文收集了2000至2021年间计算机科学领域中数据挖掘与软件工程两个方向的谷歌学术数据，利用学者数据库资源开展网络分析、数据挖掘，并通过作者合作网络识别作者关联。我们对各领域的共著网络进行了结构分析，开展了大量实验以研究发表趋势，识别各领域的关键作者与所属机构。网络分析显示，两领域网络特征明显不同，且在核心作者群体中呈现出小型子社区结构。
原文摘要 · Abstract (English)
Google Scholar is one of the top search engines to access research articles across multiple disciplines for scholarly literature. Google scholar advance search option gives the privilege to extract articles based on phrases, publishers name, authors name, time duration etc. In this work, we collected Google Scholar data (2000-2021) for two different research domains in computer science: Data Mining and Software Engineering. The scholar database resources are powerful for network analysis, data mining, and identify links between authors via authorship network. We examined coauthor-ship network for each domain and studied their network structure. Extensive experiments are performed to analyze publications trend and identifying influential authors and affiliated organizations for each domain. The network analysis shows that the networks features are distinct from one another and exhibit small communities within the influential authors of a particular domain.
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