Tag Archives: diffusion

The Relationship between Forward and Backward Diversity in CORE Datasets

In this paper we seek to better understand the relationship between forward diversity in the Cognitive Science and Educational Research literature, as well as what we call Border fields (i.e. those fields which exist at the intersection of Cognitive Science and Education Research). We find a clear and convincing relationship between forward and backward diversity in the datasets we study. Among all available explanatory variables, Integration scores claim the strongest correlation in terms of their ability to account for forward diversity. When comparing results from this study to benchmark results from a prior study (using the same indicators) the datasets in this study show a tendency to be both more integrative and diffuse.

https://doi.org/10.1007/s11192-019-03163-3

Author(s): Stephen F. Carley, Seokbeom Kwon, Alan L. Porter, Jan L. Youtie
Organization(S): Search Technology, Georgia Institute of Technology
Source: Scientometrics
Year: 2019

Modeling study of knowledge diffusion in scientific collaboration networks based on differential dynamics: A case study in graphene field

knowledge diffusion based on scientific collaboration is similar to disease propagation through actual contact. Inspired by the disease-spreading model in complex networks, this study classifies the states of research entities during the process of knowledge diffusion in scientific collaboration into four categories. Research entities can transform from one state to another with a certain probability, which results in the evolution rules of knowledge diffusion in scientific collaboration networks. The knowledge diffusion model of differential dynamics in scientific collaboration of non-uniformity networks is formed, and the relationship between the degree distribution and evolution of knowledge diffusion is further discussed, to reveal the dynamic mechanics of knowledge diffusion in scientific collaboration networks. Finally, an empirical analysis is conducted on knowledge diffusion in an institutional scientific collaboration network by taking the graphene field as an example. The results show that the state evolution of research entities in the knowledge diffusion process of scientific collaboration networks is affected not only by the evolution states of adjacent research entities with whom they have certain collaboration relationships, but also by the structural attributes and degree distributions of scientific collaboration networks. The evolution of knowledge diffusion in scientific collaboration entities with different degrees also shows different trends.

https://doi.org/10.1016/j.physa.2019.04.201

Author(s):Zenghui Yue, Haiyun Xu, Guoting Yuan, Hongshen Pang
Organization(s):Jining Medical University, Chengdu Documentation and Information Center, Chinese Academy of Sciences
Source: Physica A: Statistical Mechanics and its Applications
Year: 2019

The Diffusion of Military Technology

The impact of national defense research and development spending on overall innovation depends on the extent to which the knowledge and technologies generated by defense funding diffuse. This article uses an original data-set of patents assigned to defense-servicing organizations to investigate the diffusion of military technologies. Contrary to the predictions of the prevailing scholarship, I find no difference in the rate of diffusion between civilian and military technologies. Neither do military technologies assigned to government agencies diffuse at different rates than those assigned to firms. The overall technological experience of the patent assignee is found to be a positive predictor of the diffusion of military technologies. The effect of the prevailing intellectual property rights regime is ambivalent: when US patents are included in the sample, the effect of patent protection is positive, when the US is excluded, the effect is either non-significant or negative depending on the model specification that is utilized.

http://dx.doi.org/10.1080/10242694.2017.1292203

Author(s): Jon Schmid
Organization: Georgia Institute of Technology
Source: Defence and Peace Economics
Year: 2017