期刊
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING
卷 9, 期 1, 页码 246-257出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TETC.2018.2860051
关键词
Collaboration; Big Data; Social network services; Analytical models; Navigation; Correlation; Data models; Multidimensional network analysis; academic influence; scholarly big data; scholarly recommendation; research collaboration
资金
- National Science Foundation of China [61273232, 61772196, 61472136]
- Hunan Provincial Education Department Foundation for Excellent Youth Scholars [17B146]
This study focuses on multidimensional network analysis of scholarly big data, quantifying correlations among academic entities based on collaboration relationships, specialty-aware connections, and topic-aware citation fitness. An improved Random Walk with Restart algorithm is developed to provide research collaboration navigation for researchers. Experiments and evaluations demonstrate the practicality and usefulness of the proposed method in scholarly big data analysis.
Scholarly big data, which is a large-scale collection of academic information, technical data, and collaboration relationships, has attracted increasing attentions, ranging from industries to academic communities. The widespread adoption of social computing paradigm has made it easier for researchers to join collaborative research activities and share academic data more extensively than ever before across the highly interlaced academic networks. In this study, we focus on the academic influence aware and multidimensional network analysis based on the integration of multi-source scholarly big data. Following three basic relations: Researcher-Researcher, Researcher-Article, and Article-Article, a set of measures is introduced and defined to quantify correlations in terms of activity-based collaboration relationship, specialty-aware connection, and topic-aware citation fitness among a series of academic entities (e.g., researchers and articles) within a constructed multidimensional network model. An improved Random Walk with Restart (RWR) based algorithm is developed, in which the time-varying academic influence is newly defined and measured in a certain social context, to provide researchers with research collaboration navigation for their future works. Experiments and evaluations are conducted to demonstrate the practicability and usefulness of our proposed method in scholarly big data analysis using DBLP and ResearchGate data.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据