Mapping the Evolution and Future Trajectories of Network Mining: A Scientometric Analysis (2004-2023)

• Published in: International Symposium on Power Electronics, Electrical Drives, Automation and Motion pp. 468 - 473
• Main Authors: Wang, Xianghan, Long, Sheng, Zeng, Li, Chen, Chao, Yishan, L.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 19.06.2024
• Subjects: Bibliometrics; Citespace; Dynamics; Industries; Network Mining; Power electronics; Representation learning; Scientometric; Social networking (online)
• ISSN: 2835-8457
• DOI: 10.1109/SPEEDAM61530.2024.10609054

A Network Mining, a pivotal element of business intelligence, has been the subject of extensive study by both academic institutions and industry for an extended period. This paper presents a scientometric analysis spanning two decades to shed light on the evolving landscapes, focal points of research, and novel trends within this domain. The analysis utilized a dataset curated from the Web of Science, encompassing the years 2004 to 2023. This study goes beyond elementary scientific output assessments, employing advanced scientometric tools like CiteSpace, VOSViewer, and Bibliometrix to dissect the intellectual structure of Network Mining. The findings indicate a substantial increase in Network Mining research over the past twenty years, with a corpus of 2,110 papers emanating from 77 countries/territories. The United States, China, India, Japan, and France emerge as the top five most prolific contributors. The research domain encompasses 2,028 institutes, with the University of Illinois, Carnegie Mellon University, Chinese Academy of Sciences, Tsinghua University, and Arizona State University being the most influential. Furthermore, keywords exhibiting the most significant citation bursts, such as Social Network Mining, Anomaly Detection, Task Analysis, Network Embedding, Network Representation Learning, and Graph Neural Networks, were identified, signifying the cutting-edge directions in Network Mining research. The insights provided in this paper aim to bolster ongoing research endeavors in Network Mining, serving as a beacon for future scholarly exploration.