Amplayo, R. K., Hong, S., & Song, M. (2018). Network-based approach to detect novelty of scholarly literature. Information Sciences, 422, 542-557.

https://doi.org/10.1016/j.ins.2017.09.037

Abstract
We present a method to detect the novelty of a research paper. Because novelty in scholarly literature also examines the larger research community, a network-based approach for extracting features is proposed. Two graphs are introduced, a macro-level graph, where authors and documents are used as nodes, and a micro-level graph, where keywords, topics, and words are used as nodes. After constructing the seed graph, papers are incrementally added while changes in the graph are recorded as the feature set of a paper. An autoencoder neural network is then used as the novelty detection model. The experimental results show that the commonly used text feature representations, TF-IDF and one-class SVM, are not suitable for detecting the novelty of a research paper. Among the constructed graphs, keyword-level graph features exhibit the best performance using regression analysis as the metric. We also combine the macro-level graph, micro-level graph, and all features and find that the combination of keywords, topics, and word features perform the best using regression and citation count analysis. Other factors that could affect the citation counts, impact, and audience, are also discussed.

연구의의

본 연구는 Auto Encoder neural 네트워크를 사용하여 논문의 진위를 판별하는 방법을 제시 하고 있음 데이터를 중심으로 하여 진위를 판단하고 사회과학적 연구 역량을 증진 할수 있음.