Semantic Influence Score: Tracing Beautiful Minds Through Knowledge Diffusion and Derivative Works

Articles judged on the basis of raw citations or citation counts (or similar) are biased with “Rich gets Richer” conjecture, and continue to propagate a perceived notion of paper quality and influence among scientific communities. This perception of preferential attachment, overlooking important factors such as context and the age of the paper has been criticized recently. In this paper, we propose ‘Semantic Influence Score (SIS)’, an unbiased alternative to metrics which rely on raw citation counts. We compute the semantic influence of a paper on its derivative works by developing a multilevel influence network, which takes into account references, domain intersection and influence scores of the articles in the network. SIS provides a robust alternative to the widely used mechanism of raw citation counts i.e., the number of citations it receives.