Academic Influence Aware and Multidimensional Network Analysis for Research Collaboration Navigation Based on Scholarly Big Data

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 researcher...

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Bibliographic Details
Published in:IEEE transactions on emerging topics in computing Vol. 9; no. 1; pp. 246 - 257
Main Authors: Zhou, Xiaokang, Liang, Wei, Wang, Kevin I-Kai, Huang, Runhe, Jin, Qun
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
academic influence
Algorithms
Analytical models
Big Data
Citation analysis
Collaboration
Correlation
Data analysis
Data models
Multidimensional network analysis
Navigation
Network analysis
Random walk
research collaboration
scholarly big data
scholarly recommendation
Social network services
ISSN:2168-6750, 2168-6750
Online Access:Get full text
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Summary: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.
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ISSN:2168-6750
2168-6750
DOI:10.1109/TETC.2018.2860051