Exploring the “awakening effect” in knowledge diffusion: a case study of publications in the library and information science domain

Abstract

• Uncovered the general mechanisms that may not only be held responsible for the awakening of SBs but also the attention received by papers. • Proved that the awakening effect does exist ubiquitously, and tried to quantify it. • Proposed a super-network model that may be used to depict the knowledge diffusion trajectory and a systematic feature-searching method that can find complicated features efficiently. Inspired by “sleeping beauties in science”, we proposed that the awakening effect in knowledge diffusion is ubiquitous, whereas the “prince” paper has the strongest effect. To test this hypothesis, a three-layer super-network model depicting the knowledge diffusion trajectory is designed and the diffusion path of the awakening effect (defined on the basis of influential strength) is simulated. In detail, the model is built based on the citation network and collaboration network of 63785 publications in the library and information science domain. Through meta-paths in this super-network, the influential strength of a paper and the awakening effect from neighboring papers can be quantified into 36 numerical features. By testing the effectiveness of these features in citation counts prediction, we try to prove our hypothesis. Thus an effective predictor in machine learning is trained upon these features. Using this predictor, we showed that most neighboring papers in the super-network had effects on future citation counts. The effectiveness of these features is again demonstrated through experiments on papers with different publication years. We also did a case study on papers that were significantly affected by the awakening effect, and found that the model proposed in this paper can also be used to explain some common phenomena in knowledge diffusion. All results show that the awakening effect could be not only ubiquitous but also quantifiable.