Topic influence graph based analysis of seminal papers

Abstract

Every scientific article attempts to derive knowledge from existing literature and augment it with new insights. This dynamics of knowledge is commonly explored through references (it draws knowledge from) and citations (its impact on the field). In this paper, we propose to explore this phenomenon through construction of a topic influence graph (TIG) based on topic similarity between articles. More importantly, out of the set of possible TIGs, we determine an optimal TIG by using knowledge from citation graphs. Construction of TIG leverages traditional network analysis tools like community (sub-field) identification. In this paper, we construct the TIG on the ACL Anthology Network (AAN) dataset and leverage it to analyze the properties of seminal papers. Interestingly, we observe that seminal papers disseminate knowledge across different communities, trigger more research within its own community and apart from introducing new ideas, string together ideas from different communities.