Analysis of Computer Science Communities Based on DBLP

It is popular nowadays to bring techniques from bibliometrics and scientometrics into the world of digital libraries to analyze the collaboration patterns and explore mechanisms which underlie community development. In this paper we use the DBLP data to investigate the author's scientific career and provide an in-depth exploration of some of the computer science communities. We compare them in terms of productivity, population stability and collaboration trends.Besides we use these features to compare the sets of topranked conferences with their lower ranked counterparts.Comment: 9 pages, 7 figures, 6 table

Factors Affecting Computer Science Research Productivity and Impact in Nigeria: A Bibliometric Evidence

Computer science is a burgeoning research field and has the potential to accelerate the rate of industrialisation and subsequently, economic development. Using bibliometric data obtained from Scopus, this study employed a 15-year bibliometric analysis to highlight Nigeria’s productivity and impact trends in the computer science research landscape. Our findings are summarised as follows: First, Nigeria’s computer science research contribution and citations are meager in comparison to the global output. Secondly, international collaboration is generally weak as most collaborations are national in scope. Third, Nigeria’s computer science-related research is published in low-quality outlets, as Scopus has discontinued the indexing of most of the outlets. Although the publication growth trend is encouraging, the volume and impact of computer science-related research can improve significantly with the conduct of more quality researches that facilitated by strong international collaborations, and published in very high-quality outlets

Factors Affecting Computer Science Research Productivity and Impact in Nigeria: A Bibliometric Evidence

Computer science is a burgeoning research field and has the potential to accelerate the rate of industrialisation and subsequently, economic development. Using bibliometric data obtained from Scopus, this study employed a 15-year bibliometric analysis to highlight Nigeria’s productivity and impact trends in the computer science research landscape. Our findings are summarised as follows: First, Nigeria’s computer science research contribution and citations are meager in comparison to the global output. Secondly, international collaboration is generally weak as most collaborations are national in scope. Third, Nigeria’s computer science-related research is published in low-quality outlets, as Scopus has discontinued the indexing of most of the outlets. Although, the publication growth trend is encouraging, the volume and impact of computer science-related research can improve significantly with the conduct of more quality researches that facilitated by strong international collaborations, and published in very high-quality outlets

Structure and Dynamics of Research Collaboration in Computer Science

Complex systems exhibit emergent patterns of behavior at different levels of organization. Powerful network analysis methods, developed in physics and social sciences, have been successfully used to tease out patterns that relate to community structure and network dynamics. In this paper, we mine the complex network of collaboration relationships in computer science, and adapt these network analysis methods to study collaboration and interdisciplinary research at the individual, within-area and network-wide levels. We start with a collaboration graph extracted from the DBLP bibliographic database and use extrinsic data to define research areas within computer science. Using topological measures on the collaboration graph, we find significant differences in the behavior of individuals among areas based on their collaboration patterns. We use community structure analysis, betweenness centralization, and longitudinal assortativity as metrics within each area to determine how centralized, integrated, and cohesive they are. Of special interest is how research areas change with time. We longitudinally examine the area overlap and migration patterns of authors, and empirically confirm some computer science folklore. We also examine the degree to which the research areas and their key conferences are interdisciplinary. We find that data mining and software engineering are very interdisciplinary while theory and cryptography are not. Specifically, it appears that SDM and ICSE attract authors who publish in many areas while FOCS and STOC do not. We also examine isolation both within and between areas. One interesting discovery is that cryptography is highly isolated within the larger computer science community, but densely interconnected within itself.