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Zhang, Y.; Zhang, G.; Zhu, D.; Lu, J.: Scientific evolutionary pathways : identifying and visualizing relationships for scientific topics (2017)
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- Abstract
- Whereas traditional science maps emphasize citation statistics and static relationships, this paper presents a term-based method to identify and visualize the evolutionary pathways of scientific topics in a series of time slices. First, we create a data preprocessing model for accurate term cleaning, consolidating, and clustering. Then we construct a simulated data streaming function and introduce a learning process to train a relationship identification function to adapt to changing environments in real time, where relationships of topic evolution, fusion, death, and novelty are identified. The main result of the method is a map of scientific evolutionary pathways. The visual routines provide a way to indicate the interactions among scientific subjects and a version in a series of time slices helps further illustrate such evolutionary pathways in detail. The detailed outline offers sufficient statistical information to delve into scientific topics and routines and then helps address meaningful insights with the assistance of expert knowledge. This empirical study focuses on scientific proposals granted by the United States National Science Foundation, and demonstrates the feasibility and reliability. Our method could be widely applied to a range of science, technology, and innovation policy research, and offer insight into the evolutionary pathways of scientific activities.
- Source
- Journal of the Association for Information Science and Technology. 68(2017) no.8, S.1925-1939
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Zhang, Y.; Wu, M.; Zhang, G.; Lu, J.: Stepping beyond your comfort zone : diffusion-based network analytics for knowledge trajectory recommendation (2023)
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- Abstract
- Predicting a researcher's knowledge trajectories beyond their current foci can leverage potential inter-/cross-/multi-disciplinary interactions to achieve exploratory innovation. In this study, we present a method of diffusion-based network analytics for knowledge trajectory recommendation. The method begins by constructing a heterogeneous bibliometric network consisting of a co-topic layer and a co-authorship layer. A novel link prediction approach with a diffusion strategy is then used to capture the interactions between social elements (e.g., collaboration) and knowledge elements (e.g., technological similarity) in the process of exploratory innovation. This diffusion strategy differentiates the interactions occurring among homogeneous and heterogeneous nodes in the heterogeneous bibliometric network and weights the strengths of these interactions. Two sets of experiments-one with a local dataset and the other with a global dataset-demonstrate that the proposed method is prior to 10 selected baselines in link prediction, recommender systems, and upstream graph representation learning. A case study recommending knowledge trajectories of information scientists with topical hierarchy and explainable mediators reveals the proposed method's reliability and potential practical uses in broad scenarios.
- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.7, S.775-790
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Song, M.; Kim, S.Y.; Zhang, G.; Ding, Y.; Chambers, T.: Productivity and influence in bioinformatics : a bibliometric analysis using PubMed central (2014)
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- Source
- Journal of the Association for Information Science and Technology. 65(2014) no.2, S.352-371
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Li, R.; Chambers, T.; Ding, Y.; Zhang, G.; Meng, L.: Patent citation analysis : calculating science linkage based on citing motivation (2014)
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- Source
- Journal of the Association for Information Science and Technology. 65(2014) no.5, S.1007-1017
