中文摘要：

科学合作是促进知识传播和共享的重要途径,已有研究表明,地理因素是影响科学合作的主要因素之一。然而,目前针对该问题的研究大多只是从科学计量学的角度,对科学合作强度与地理距离的函数关系进行描述,无法揭示科学合作在空间上的分布特征和内部差异性。因此,本文从地理学的角度,以中国雾霾研究的合作网络为例,通过对文献题录中的位置信息进行解析,将虚拟的科学合作网络映射到地理合作网络。在此基础上,提出了一种考虑地理距离的科学合作网络社区发现方法,挖掘科学合作网络中蕴含的空间聚类特征,从而对科学合作的地域倾向性进行反映。通过比较发现,基于合作频次与地理距离的社区发现算法,能够使社区内部的平均地理距离最小而合作强度最大,既反映了科学合作在地理上的近似性,又体现了科学合作强度特征。该方法能够直观地揭示科学合作中隐含的空间分布模式和联系,对其他复杂网络的地理社区划分也有一定的借鉴意义。

英文摘要：

Scientific collaboration is an important way of knowledge dissemination and sharing. Researches have showed that geographic factor is one of the main factors that influencing scientific collaboration. However, most of related researches have just quantitatively described the functional relationship between collaboration strength and geographic distance from the perspective of Scientometrics. As a result, it can hardly detect the spatial characteristics and relationship of scientific collaboration. In this paper, for the purpose of mining spatial pattems in scientific collaboration network, geographical preference of scientific collaboration was studied from the view of geography. Taking the haze research network in China for example, the location information was extract- ed from bibliographic data and then the virtual scientific collaboration network can be mapped into geo-collabo- ration network by using geocoding service. Based on this, a distance-based method for community detection of scientific collaboration network was proposed to explore the spatial cluster pattern in scientific collaboration. Using modified Louvain community detection algorithm, two different variables were used as weight factor to detect communities. The results showed that, the community detection algorithm considering collaboration frequency and geographic distance can make the average geographic distance minimum and the Salton index maximum inside community, which both reflect the geographical preference and collaboration strength of scientific collabo- ration. This method can effectively explore the spatial pattern and relationship in scientific collaboration network, and represent geographical preference of scientific collaboration in a quantitative and qualitative way. In addition, it is a novel method of introducing geographic location and geographic distance into complex network analysis. We hope that it will not only be helpful for scientific collaboration network, but also can be applied to other complex network for geographic