The Artificial Immune Systems Domain: Identifying Progress and Main Contributors Using Publication and Co-Authorship Analyses

Abstract

Much can be learned about the progress, fathers and future of a scientific domain from the analysis of a collection of relevant articles and their corresponding authors. Here, we study the highly interdisciplinary domain of Artificial Immune System (AIS) since its birth, a couple of decades ago. We apply Social Network Analysis to the coauthorship network of the most comprehensive publicly accessible AIS bibliography. We automatically extract publication dates and author names from the bibliography and evaluate authors with the highest degree (uniue collaborations) and centrality (influence). Our results highlight the relative growth of publication volume and identify significant contributors in the AIS field. Furthermore, our findings are not only encouraging for the AIS community but may be useful for analyses of other scientific communities and leading contributors therein. Keywords: Artificial Immune Systems, Social Network Analysis, Coauthorship, Information Extraction, Text Mining.