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.

翻译：Google Scholar 是跨学科获取学术文献的重要搜索引擎之一。其高级检索功能支持根据短语、出版商名称、作者姓名、时间范围等条件提取文献。本研究收集了计算机科学领域两个不同研究方向——数据挖掘与软件工程——在 Google Scholar 中 2000 年至 2021 年的数据。该学术数据库资源为网络分析、数据挖掘及通过作者合作网络识别学者关联提供了强大支持。我们分别构建了两个领域的作者合作网络，并深入研究了其网络结构。通过大量实验分析了各领域的发表趋势，识别了具有影响力的学者及其所属机构。网络分析结果表明，两个领域的网络特征存在显著差异，且在特定领域的高影响力学者内部呈现出小型社群聚集现象。