Universal trajectories of scientific success

Success of a scientific entity generally undergoes myriad vicissitudes, resulting in different patterns of success trajectories. Understanding and characterizing the rise and fall of scientific success is important not only from the perspective of designing new mathematical models but also to enhance the quality of various real-world systems such as scientific article search and recommendation systems. In this paper, we present a large-scale study of the subject by analyzing the success of two major scientific entities-papers and authors-in Computer Science and Physics. We quantify success in terms of citations and in the process discover six distinct success trajectories which are prevalent across multidisciplinary datasets. Our results reveal that these trajectories are not fully random, but are rather generated through a complex process. We further shed light on the behavior of these trajectories and unfold many interesting facets by asking fundamental questions-which trajectory is more successful, how significant and stable are these categories, what factors trigger the rise and fall of trajectories? A few of our findings sharply contradict the well-accepted beliefs on bibliographic research such as Preferential Attachment, first-mover advantage. We believe that this study will argue in favor of revising the existing metrics used for quantifying scientific success.