A novel approach to measuring science-technology linkage: From the perspective of knowledge network coupling

Research Ideas Discovery via Hierarchical Negative Correlation

A new research idea may be inspired by the connections of keywords. Link prediction discovers potential nonexisting links in an existing graph and has been applied in many applications. This article explores a method of discovering new research ideas based on link prediction, which predicts the possible connections of different keywords by analyzing the topological structure of the keyword graph. The patterns of links between keywords may be diversified due to different domains and different habits of authors. Therefore, it is often difficult for a single learner to extract diverse patterns of different research domains. To address this issue, groups of learners are organized with negative correlation to encourage the diversity of sublearners. Moreover, a hierarchical negative correlation mechanism is proposed to extract subgraph features in different order subgraphs, which improves the diversity by explicitly supervising the negative correlation on each layer of sublearners. Experiments are conducted to illustrate the effectiveness of the proposed model to discover new research ideas. Under the premise of ensuring the performance of the model, the proposed method consumes less time and computational cost compared with other ensemble methods.

Do academic inventors have diverse interests?

Academic inventors bridge science and technology, and have attracted increasing attention. However, little is known about whether they have more diverse research interests than researchers with a single role, and whether their important position for science–technology interactions correlates with their diverse interests. For this purpose, we describe a rule-based approach for matching and identifying academic inventors, and an author interest discovery model with credit allocation schemes is utilized to measure the diversity of each researcher’s interests. Finally, extensive empirical results on the DrugBank dataset provide several valuable insights. Contrary to our intuitive expectation, the research interests of academic inventors are the least diverse, while those of authors are the most. In addition, the important position of the researchers has a certain relation with the diversity of research interests. More specifically, the degree of centrality has a significant positive correlation with the diversity of interests, and the constraint presents a significant negative correlation. A significant weaker negative correlation can also be observed between the diversity of research interests of academic inventors and their closeness centrality. The normalized betweenness centrality seems be independent from interest diversity. These conclusions help understand the mechanisms of the important position of academic inventors for science–technology interactions, from the perspective of research interests.