Using Complex Network Metrics for Evaluating the Influence of Conference and Journal Papers in Computer Science<br/>

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Using Complex Network Metrics for Evaluating the Influence of Conference and Journal Papers in Computer Science

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 43656 (19)
University: Sharif University of Technology
Department: Computer Engineering
Advisor(s): Jalili, Mahdi
Abstract:
Journals and conferences in computer science are the major venue for publishing new achievement in the field. It is an expert opinion that a number of top conferences in computer science are even more important than journals. In this work we aim at studying this in terms of citation analysis. To this end, we took 100 top journals and 63 top conferences and extracted their citation graph through Scopus dataset. We then constructed the citation graph in which the nodes were the journals and conferences and the links corresponded to the citations of the papers. We used various measures to rank the nodes in the graph. The ranking methods included Prestige, PageRank, Eigenfactor, HITS and SALSA. We also used a number of graph theory metrics for computing node centralities (e.g., betweenness and vulnerability measure). Our analysis showed that a number of conferences were among the top-20 nodes in the graph. Our results give an evidence that the common belief in the computer science community (some conference are higher in rank than journals) is indeed correct
Keywords:
Pages Ranking ; Parallel Pagerank Algorithm ; Computer Science ; Computer Science Journals ; Computer Science Conferences ; Citation Graph

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