How can we personalize the discovery of research to support scholarly reading? The rapid expansion of scholarly literature across disciplines presents a growing challenge for researchers, educators, and students striving to stay informed and to make meaningful contributions. This work introduces a network-based methodology for identifying what papers in a research area a researcher should read. By leveraging topological metrics such as k-core and community detection, we offer a scalable and objective framework for identifying the communities that form the body of work and their representative papers. Building on this foundation, we propose a personalized literature review methodology that, based on a user’s academic background and chosen keywords, recommends the most relevant papers to read.