Mining Citation Networks to Detect and Analyze Cliques and Cartel-Like Patterns

Joseph G. Davis
2018 Zenodo  
With the growing emphasis on metrics such as citation count and h-index for research assessment, several reports of gaming and cartel-like formations for boosting citation statistics have emerged. However, such cartels are extremely difficult to detect. This paper presents a systematic approach to visualizing and computing clique and other anomalous patterns through ego-centric citation network analysis by drilling down into the details of individual researcher's citations. After grouping the
more » ... fter grouping the citations into three categories, namely, self- citations, co-author citations, and distant citations, we focus our analysis on the outliers with relatively very high proportion of self- and co-author citations. By analyzing the complete co-authorship citation networks of these researchers one at a time along with all the co-authors and by merging these networks, we were able to isolate and visualize cliques and anomalous citation patterns that suggest plausible collusion. Our exploratory analysis was carried out using the citation data from Web of Science (Clarivate Analytics) for all the highly cited researchers in Computer Science, Engineering, and Physics. Some of the exciting research opportunities in citation analytics are also outlined.
doi:10.5281/zenodo.4066335 fatcat:vtowg2eaifbnle5tecmbcxciwa