Title
Applications of Machine Learning in Analysis of Citation Network.
Abstract
Research standard and quality should be continuously monitored to direct progress of science in right direction. With exponential growth and continuous expansion in citation network, manual and static analysis is becoming insignificant. To fill in the gap, application of machine learning models might prove to be useful. In this paper, we propose some of the problems that we intend to solve using machine learning. Among various applications outlier analysis for early detection of anomalies in citation network, long term prediction of high impact and seminal authors, papers and field of study, deriving inherent features on diverse temporal and demographic scale governing citation structure etc. Starting with empirical analysis of open academic graph dataset, we try to understand the complex relational citation structure of entities. As a preliminary step, we do time series clustering of citation data and study characteristics of diverse profiles of citation curves. When compared to static classification in past literature, we overcome drawbacks of past study and get better insights.
Year
DOI
Venue
2019
10.1145/3297001.3297053
COMAD/CODS
Keywords
Field
DocType
Machine learning models, bibliographic dataset analysis, clustering, temporal citation profiles
Graph,Long-term prediction,Computer science,Static analysis,Citation,Outlier,Citation network,Artificial intelligence,Cluster analysis,Machine learning,Exponential growth
Conference
CitationsĀ 
PageRankĀ 
ReferencesĀ 
0
0.34
5
Authors
3
Name
Order
Citations
PageRank
Dinesh Kumar Pradhan100.34
Joyita Chakraborty200.34
Subrata Nandi37121.37