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Abstract
Citation impact is commonly assessed using direct, first-order citation relations. We consider here instead the indirect influence of publications on new publications via citations. We present a novel method to quantify the higher-order citation influence of publications, considering both direct, or first-order, and indirect, or higher-order citations. In particular, we are interested in higher-order citation influence at the level of disciplines. We apply this method to the whole Web of Science data at the level of disciplines. We find that a significant amount of influence—42%—stems from higher-order citations. Furthermore, we show that higher-order citation influence is helpful to quantify and visualize citation flows among disciplines, and to assess their degree of interdisciplinarity.
Highlights
New knowledge builds on previous knowledge: this is a central tenet of science
Our proposed method is related to the well-known PageRank algorithm (Brin and Page 1998; Franceschet 2011; Waltman and Yan 2014), but it is focused on quantifying higher-order citation influence
We proposed instead here to quantify citation influence beyond direct citations by using higher-order citations, that is citations chains of arbitrary length among pairs of publications
Summary
New knowledge builds on previous knowledge: this is a central tenet of science. A publication relies on previous publications and cites them to acknowledge this debt (Merton 1957). We aim at understanding the interplay of first and higher-order influence across academic disciplines In this contribution we define higher-order citations as citations chains of arbitrary length among pairs of publications, and show how the higher-order citation matrix among disciplines can be computed in an iterative and efficient way. Our proposed method is related to the well-known PageRank algorithm (Brin and Page 1998; Franceschet 2011; Waltman and Yan 2014), but it is focused on quantifying higher-order citation influence. We apply this novel definition to the Web of Science dataset between years 2000 and 2016 included (17,932,523 publications and 190,550,206 citations among them). We observe this added value by clustering disciplines into larger communities, finding disciplines that act as brokers among communities, and distinguishing between interdisciplinary and autarchic disciplines
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