Against the background of economic globalization and technological development, innovation network has been a heated topic in the field of economic geography research. However, the scale of research of innovation network remains debatable. Among the global, nation, and local scales, which one is important? The concept of “glocalization” provides a new perspective to this research. Glocalization refers to the twin process whereby, firstly, institutional arrangements shift from the national scale both upwards to the global scale and downwards to local configurations and, secondly, economic activities and inter firm networks are becoming simultaneously more localized/ regionalized and transnational. Based on solid theoretical reviews, this article defines the concept of glocal innovation network and then discusses the main issues and research methods of glocal innovation networks. In this article, glocal innovation network is defined as the sum of knowledge network channels of various innovators, for example, firms, universities, and research institutes, which increasingly connect globally scattered innovation resources together. Local innovation networks, which are the sub-networks of global innovation networks, are connected by trans-local knowledge flows. Glocal innovation networks are organized by the negotiation among industrial associations and technology alliances and their members. Network knowledge measurement is the suitable method to analyze the structure, evolution, and mechanism of glocal innovation network. The concept of glocal innovation networks provides a new perspective to analyze the approaches of local/regional innovation capabilities promotion and economic development by utilizing global and local knowledge. We conclude that existing research remains at the stage of conceptual discussion and case studies. Therefore, the following issues should be further studied: (1) glocal innovation network evolution dynamics and its connection with economic development; (2) comparative study of glocal networks of different industries and technologies; (3) characteristics of Chinese glocal innovation networks, which can provide empirical evidence of latecomer regions in the catching-up process for further theoretical discussion.

全球化、创新驱动是新时代的重要特征之一,创新网络成为经济地理学者关注的热点领域之一。在评述现有创新网络研究成果的基础上,本文界定了全球—地方创新网络的内涵和特征,论述了其类型、结构、作用机理和分析方法,并得出结论：全球创新网络与地方创新网络是不可分割的有机体,地方创新网络是全球创新网络的子系统,知识流是创新网络各主体之间联系的重要纽带,行业协会、技术联盟与成员之间的多次协商是全球—地方创新网络的重要组织方式,而网络知识测量方法则能较好地实现定性分析结论与统计计算结论的融合,能较好地刻画、模拟全球—地方创新网络的形态、结构、演变和机理。从服务国家建设和推动中国创新地理学发展的目标出发,有必要开展基于中国国情和视角的全球—地方创新网络机理与区域经济增长之间互动关系的研究,启动不同产业领域的全球—地方创新网络的比较分析,检验网络知识测量方法的可靠性和准确性。

Against the backdrop of new urbanization and high-quality development, the spatial structure of urban agglomerations and their polycentric development have become a hot issue in urban studies. Based on the publication data of Chinese scholars from 2012 to 2016 derived from the Web of Science (WOS), this research assessed the knowledge morphological polycentricity and functional polycentricity of 19 urban agglomerations in China by employing Taylor’s polycentricity measurement through the lens of scientific knowledge output and knowledge collaboration network. After quantifying and analyzing the development status, this research explored the determinants of polycentric urban development in China and made some policy recommendations for future development. The results show that: (1) There exists an obvious spatial heterogeneity in both knowledge resources distribution and the knowledge collaboration patterns among different urban agglomerations. The polycentric development of urban agglomerations in the east is better than their central and western counterparts. The overall development of morphological and functional polycentricities is consistent. (2) Urban agglomerations can be divided into four categories: Ⅰ. urban agglomerations with both morphologically and functionally polycentric development, which is typical for the Yangtze River Delta urban agglomeration; Ⅱ. urban agglomerations with morphologically monocentric but functionally polycentric development consisting of the Pearl River Delta urban agglomeration; III. urban agglomerations with both morphologically and functionally monocentric development, which is typical for the northern Ningxia urban agglomeration along the Yellow River; IV. urban agglomerations with morphologically polycentric but functionally monocentric development consisting of the Chengdu-Chongqing urban agglomeration. (3) The knowledge polycentric development of China’s urban agglomerations is driven by both market and policies. For urban agglomerations with the development of knowledge morphological monocentricity, emphasis should be put on the scientific planning and layout of knowledge innovation resources; for those with relatively functionally monocentric development, local governments need to break the barriers of knowledge flows and form a diversified collaboration and sharing mechanism.

在新型城镇化和高质量发展背景下,城市群的空间结构及其多中心发展成为城市研究的热点议题。本文基于Web of Science（WOS）数据库中国学者2012—2016年的论文发表数据,采用Taylor的多中心性测度,从知识规模和知识网络层面,对中国19个城市群知识形态多中心与功能多中心进行量化,进而分析不同城市群知识多中心的发展状况、原因及对策,主要结论如下：①中国城市群的知识规模分布和知识合作格局均具有较强的空间异质性,东部城市群知识多中心发育优于中西部城市群,城市群形态与功能多中心发展整体匹配度高。②以中国19个城市群知识形态和功能多中心度的平均值为界,可将城市群分为4类：Ⅰ知识形态与功能均相对多中心发展的城市群,以长三角城市群最为典型;Ⅱ知识形态相对单中心但功能相对多中心发展的城市群,如珠三角城市群;Ⅲ知识形态与功能均相对单中心发展的城市群,以宁夏沿黄城市群最为典型;Ⅳ知识形态相对多中心但功能相对单中心发展的城市群,如成渝城市群。③中国城市群知识多中心发展是市场驱动和政策引导共同作用的结果,对于知识形态相对单中心发展的城市群,需重点加强知识创新资源的合理规划与布局;对于知识功能相对单中心发展的城市群,需积极打破知识流通壁垒,形成多元合作共享机制。

Inter-urban linkage is traditional research field of urban geography. With the increasing importance of innovation in city, inter-urban linkage of innovation has aroused the interesting of numerous sholars, some of which have examined the field through direct surveyed approach by co-author published papers or co-author patents granted, however, this approach is limited because it lacks data of the inter-urban and the rusults of survey may not present the comprensive inter-urban innovation situation of the cities. Therefore, we employ a indrect approach, using revised gravity model to map the pattern of inter-urban innovation linkage of Chinese major cities. China takes constructing the innovation country as the core strategy, and urban innovation as the core contents of national innovation system, so urban innovation linkage is an important part of China’s national innovation system. However, a number of issues, such as the current sitation of urban innovaiton linkage, and the pattern and laws of inter urban innovation have rarely been studied. This article will try to study the inter urban innovation linkage among major Chinese cities so as to find innovation source cities and innovation nodes cities in urban innovation system and the general pattern of the inter urban innovation, to promote the complementary and optimization of urban innovation function and to plan the circle of China urban innovation. Based on the review of the literatures of innovation linkage and theoretical analysis, through establishing a set of measureement of index, this article defines ourward innovation linkage of scale and measures innovation linkage and innovation pattern among Chinese major cities. The research shows: 1) the general pattern of urban innovation linkage in East China is stronger and that in West China is weak, and a "Golden Triangle innovation linkage" pattern has formed in the coastal area of China, which takes Shanghai, Nanjing and Hangzhou as the vertex, while Beijing-Tianjin and Guangzhou-Shenzhen as two points. 2) the city innovation linkage presents obvious hierarchy, the cities, such as Beijing, Shanghai, Guangzhou, Shenzhen, Tianjin and Chongqing have national innovation influence with extensive innovative linkage with the other cities in China, while cities, such as Nanjing, Hangzhou, Wuhan, Zhengzhou, Jinan, Qingdao, Dalian and Xi'an have regional innovation influence. 3) in terms of the East Coastal main economic circle in China, the Zhujiang River Delta economic circle has the strongest internal innovation linkage, but less outward innovation radiation; the Changjiang River Delta economic circle has very strong internal innovation linkage with strong external innovation linkage with the cities of Huan Bohai economic circle, while the cities of Beijing, Tianjin and Tangshan have very strong innovation linkage, and with strong outward radiation to the Changjiang River Delta economic circle. This article examines the general innovation linkage pattern among Chinese major cities considering two important elements of distance among cites and scale of urban innovation, but some elements, such institution and policies which may influence the innovation linkage have not been examined, it will be put consideration in future studies.

对国内外城市创新联系综述及理论分析的基础上,通过一组测度指标,界定了城市外向创新联系规模,采用引力模型,测度了中国主要城市间的创新联系强度及格局。研究表明：中国主要城市创新联系格局基本为东强西弱,东部地区城市创新联系格局显现出以上海、南京、杭州为顶角,以北京、天津,以广州、深圳为2个底角的创新联系“金三角”。城市创新联系在空间上呈现明显的等级性：北京、上海、广州、深圳、天津、重庆等与中国的许多城市有广泛的创新联系,具有全国创新影响力;南京、杭州、武汉、郑州、济南、青岛、大连、西安等成为地区性的城市创新联系节点,具有区域性的创新影响力。在创新联系较强的东部沿海主要的经济圈,珠江三角洲经济圈城市间创新联系最强,但外向辐射力有限;长江三角洲经济圈内部创新联系较强,并与环渤海经济圈有较强的创新联系, 环渤海经济圈内部北京、天津、唐山具有较强的创新联系,外向辐射以长江三角洲的城市为主。对中国创新联系格局规律的揭示,更进一步强化了中国创新城市体系中城市的作用,并为规划与建立中国创新都市圈提供依据。

With the rapid development of knowledge economy, a number of important shifts are emerging in urbanization pattern across the world. Traditional urbanization theory has become hard to interpret these changes on a global scale, and “innovation” is a core concept to explain the new changes of the urbanization dynamics. As one of the important contents of urban geography, urbanization dynamic needs to turn from the general population flows between rural and urban areas into emphasizing high skilled migrants flow among cities and regions research. Against this background, we propose a conception of innovation-based urbanization. Using this concept, this study analyzes the spatial distribution characteristics of high skilled migrants and cultural diversity on urban innovation in China, based on the data of the sixth census of 270 cities at the prefecture level or above in China. This study measures the extent to which highly skilled migrants and cultural diversity increase urban innovation, by using spatial Durbin method to construct urban innovation regression model, to support the concept of innovation-based urbanization. The result shows that: first of all, the concept of innovation-based urbanization conforms to the development of knowledge economy, which emphasizes the migration process of highly skilled labor to cities. It helps to promote the changes in urban functions and landscapes and the expansion of urban knowledge activities, which undelines new dynamics of urban development, innovative landscape. Secondly, innovation urbanization based on highly skilled migrants flow is an important driving force for the development of Chinese cities, especially for eastern coastal cities and capital cities. Thirdly, the scale of highly skilled migrants flow and the level of urban cultural diversity in China both have been demonstrated of having positive effect on urban innovation output. With other conditions unchanged, a 10% increase in the number of urban highly skilled migrants and urban cultural diversity will directly result in an increase of 3% and 2% in urban innovation output respectively. This research has deepened our understanding and awareness of the openness and dynamics of the regional innovation system, and it has also provided an important theoretical basis for the formulation of urbanization and urban development policy under knowledge economy.

The interactive relationships between cities in the knowledge economy era have attracted much attention. Researchers have applied a range of methods to explore intercity innovative relationships and associated network characteristics. It nevertheless remains unclear just how intercity innovative relationships can be theoretically constructed based on knowledge flow space and how further scientific simulation methods can be designed. Research questions in this area have rarely been explored in detail, an issue which has inevitably placed obstacles on further exploration. A framework for the theoretical construction of intercity innovative relationships is presented in this study; the basis for this research is that an intercity innovation network is essentially a 'soft network', distinct from a 'hard network'. These interconnections are founded on a subjective relationship construction process and therefore necessitate scale transformation from 'point-point' connections between innovative subjects in different cities with respect to 'city-city' interactions. At the same time, this transformation process is prone to exaggerations and deviations from objective intercity innovative relationships and therefore exerts considerable influence on the accuracy of results such that constructions must be entirely theoretical. Four construction methods for intercity innovative relationships and network simulation are summarized in this study, including an intercity undirected network based on cross-city co-operations between scientific and technological achievements, an intercity directed network based on the cross-city transfer of scientific and technological achievements, an intercity innovation network based on the cross-city flow of high-end talents, and an intercity innovation network based on the multi-city distribution of innovative enterprises and institutions. Simulation tests were then undertaken using relevant data to reflect aspects of these relationships. The results of this analysis are conducive to further exploration of global and regional innovative spatial patterns from the perspective of urban geography and intercity relationships and provide a theoretical and methodological foundation for further research on intercity innovation networks.

知识经济时代城市间的创新关系是新时代城市间相互作用关系的新内涵,研究者尝试采用各种方法探索城市间创新关系及其网络特征。然而,如何从理论上建构知识流动空间的城市间创新关系?如何设计更加合理的城市间创新网络模拟方法?这些问题却少有专门探讨。基于相关研究,本文提出了城市间创新关系构建的理论框架,认为城市间创新网络本质上是区别于“硬网络”的“软网络”,是一种主观的关系建构过程,需要经过异城创新主体间的点—点关系向城—城之间关系的尺度转换,这一转换过程容易发生夸大或偏离城市间客观存在的创新关系,对结果的精确度产生很大影响,应对关系建构给予充分理论论证;本文论述了4种城市间创新关系建构和网络模拟方法,包括科技成果异城合作的城市间无向网络构建方法、科技成果转让转移的城市间有向网络构建方法、高端人才跨城移动的城市间创新网络建构方法和创新企业机构多城分布的城市间创新网络建构方法,并运用相关数据进行了模拟试验与结果展示,来反映城市间创新关系的不同方面。本研究有助于推动从城市地理学视角和城市关系的维度探讨全球/区域的创新空间格局,为城市间创新网络研究提供理论和方法支撑。

The Chinese urban system is currently experiencing a fundamental shift, as it moves from a size-based hierarchy to a network-based system. Contemporary studies of city networks have tended to focus on economic interactions without paying sufficient attention to the issue of knowledge flow. Using data on co-authored papers obtained from China Academic Journal Network Publishing Database (CAJNPD) during 2014-2016, this study explores several features of the scientific collaboration network between Chinese mainland cities. The study concludes that: (1) the spatial organization of scientific cooperation amongst Chinese cities is shifting from a jurisdiction-based hierarchical system to a networked system; and (2) several highly intra-connected city regions were found to exist in the network of knowledge, and such regions had more average internal linkages (14.21) than external linkages (8.69), and higher average internal linkage degrees (14.43) than external linkage degrees (10.43); and (3) differences existed in terms of inter-region connectivity between the Western, Eastern, and Central China regional networks (the average INCD of the three regional networks were 109.65, 95.81, and 71.88). We suggest that China should engage in the development of regional and sub-regional scientific centers to achieve the goal of building an innovative country. Whilst findings reveal a high degree of concentration in those networks - a characteristic which reflects the hierarchical nature of China’s urban economic structure - the actual spatial distribution of city networks of knowledge flow was found to be different from that of city networks based on economic outputs or population.

At present, China’s city innovation system is gradually taking shape. As the core component of innovation resources, innovative technology represented by patents has become the focus of competition among all cities. Its gathering and diffusion channels urgently need to build a compatible city technology transfer system. The construction of a national technology transfer system in line with the law of science and technology innovation, the law of technology transfer and the law of industrial development is an inevitable choice for serving the strategy of innovation development. Based on data mining from National Intellectual Property Office of China, the heterogeneities and its evolution characteristics of city innovation network depicted by patent transfer in topology and space from 2001 to 2015 were sketched using lots of visualizing tools such as Pajek, Gephi, VOSviewer, ArcGIS, and so on. Topologically, from 2001 to 2015, with the increasing number of cities involved in technology transfer, China city innovation network has emerged a significant small-world feature with the smaller average path length and the extremely large cluster coefficient compared to its counterpart. In addition, the entire network presents a core- periphery structure with hierarchies, which dominated by Beijing, Shanghai and Shenzhen. Spatially, the quadrilateral pattern of China city innovation network based on the triangular structure is gradually formed. Last but not least, the growth mechanism of city innovation network were also verified by correlational analysis, negative binomial regression approach and gravity model of STATA. The growth of city innovation network in China is significantly related to the technological innovation strength represented by the number of patent application. The findings further confirm that geographical distance has weakened cross-city patents transfer. Meanwhile, the similarity of economic development and industrial structure between cities are also important factors influencing the growth of city innovation network.

以国家知识产权局专利转让记录为数据源,采用大数据挖掘与分析技术、空间分析技术、复杂网络模型和负二项回归模型,系统描绘了2001~2015年中国城市创新网络的拓扑结构、空间结构和生长机制。研究发现：① 中国城市创新网络规模迅速扩张,在两极分化下涌现出显著的小世界性和等级层次性,以北京、上海、深圳为核心的核心-边缘格局不断强化;② 以三角结构为基础的中国城市创新网络的四边形格局逐渐形成,京津、长三角、珠三角是中国城市专利转移的核心三角;③ 中国城市创新网络的生长与城市科技创新实力显著相关,且受到地理距离的阻抗作用,凸显出强烈的地理邻近性,另外城市经济发展水平相似度和产业结构相似度也是影响城市创新网络生长的重要因素。

Improving the connectivity of multi-sector enterprises at a cross-regional level can enhance knowledge and technology transfer and stimulate innovation and synergies among cities. Therefore, the study of city networks, which comprise a large number of multi-sector enterprises, can provide an important knowledge base for innovation and development at the regional and national levels. Based on an evaluation of innovative enterprises in China by authoritative institutions, data were collected on 1778 multi-sector enterprises, which included details on the headquarters, the branches (a total of 30,625) and the locations. A city-based network for the country was established, using the data for the multi-sector linkages and a model for the headquarters-branches, to explore the network connectivity characteristics via social network analysis, the GIS method and the spatial interactive model. The results showed that (1) although the network covered 353 cities across China, the spatial distribution of the network was extremely uneven. For instance, a diamond-shaped connectivity pattern emerged gradually as the network hierarchy decreased. The Beijing-Tianjin-Hebei region, the Yangtze River Delta and the Pearl River Delta were found to be the three key hubs of the network. (2) The intercity linkages between innovative enterprise sectors (innovative enterprise flows), had a clear administrative center and a provincial boundary effect. Moreover, the innovative enterprises were strongly attracted to the municipalities and provincial capitals. Beijing was at the heart of the network, followed by Shanghai and Shenzhen. (3) Differences existed in regional connectivity. There was a striking difference between the eastern region and the central-western region. The former had a high connectivity with respect to both the internal and the external networks, while the latter had lower connectivity for the internal network but a higher connectivity at the external level. At the same time, although the network structures for all the three eastern megalopolises showed strong cohesion, their connectivity characteristics were quite different. (4) The input and output of innovative enterprise flows were to varying degrees influenced by the indicator attributes for each city, and these in turn were related to the administrative hierarchy, the economic strength and the innovative environment of the region, as well as proximity indicators, which were related to geographical, technological and institutional factors. Foreign capital was not conducive to innovative enterprise flows and to the formation of innovative intercity company-based networks in China.

多部门创新企业的跨城布局能够增进城市间的知识流动与创新联系,大量创新企业部门关联构成的城市网络是理解国家创新格局的重要认知基础。本文运用1778家国家级创新企业部门关联数据和“总部—分支机构”方法建立中国城市间有向关系矩阵,借助社会网络分析、GIS技术及空间交互模型方法,对创新企业建构的中国城市网络联系特征进行分析。结果发现：① 基于创新企业的中国城市网络联系覆盖广但极不均质,菱形联系格局在次级网络中逐步显现,京津冀、长三角和珠三角城市群是网络联系的核心枢纽。② 城市间创新企业流具有明显的行政中心指向特征与省级边界效应,直辖市及省会城市对创新企业流具有较强吸引力,北京是网络联系最强核心,上海和深圳次之。③ 城市网络区域异质性突出,东部区域“内联外控”与中、西部区域“内弱外强”的联系格局呈现鲜明对比,东部区域内沿海三大城市群网络的联系结构也存在较大差异。④ 城市的行政等级、经济水平和创新环境等属性指标,以及城市间地理、技术、制度的邻近性关系指标,都对城市吸收和输出创新企业流产生了不同程度的影响,外资并不利于促进中国城际创新企业流和网络的形成。

With the rise of the knowledge-based economy in the 1980s, knowledge (including code and tacit knowledge) as the backbone of innovation has become a key factor affecting production process. Cities have gathered not only a large number of professionals, universities and research institutions, but also a great many producers and consumers, which provides the premise for the innovation actions. City's knowledge storage and its position in the regional knowledge network play an important role in comprehensive competitiveness. Published papers and patents are main outcomes of innovation, which are used to evaluate the urban innovation capability. Moreover, co-publications and co-patents are not only the form of knowledge spillover, but also the key indicators to measure regional innovation. Taking the co-publication and co-patent in the field of biotechnology in China during 2000-2009 as the original data, we built scientific knowledge network (SKN) and technological knowledge network (TKN) between cities. From the perspective of complex networks and geospatial analysis, we explored the temporal and spatial complexity of knowledge spillovers combining the indicators of whole network structure, ego network, power-law, hubs and so on. The results show that: firstly, the nodes degree distribution of SKN and TKN is consistent with the power-law distribution, which means that the both networks not only have a scale-free network structure, but also present a preferential attachment rule when the cities choose the cooperation partner. Secondly, central cities have an obvious hierarchical structure, and are featured by a "big scattered and small gathering" spatial pattern in SKN, while the TKN is not showing this feature. From the view of central city ego network, the cooperation develops between the coastal capital cities at first, and then turns to inter-regional cooperation, such as Yangtze River Delta, Pearl River Delta, and inter-regional knowledge spillovers is obvious in SKN. The central cities and its partners are still in the coastal city instead of western provincial capitals, and inter-regional knowledge spillovers are not significant in TKN. Thirdly, the temporal evolution of central cities and its ego-network presents hierarchical diffusion and contagious diffusion, and conforms to law of grades process in SKN. The TKN is dominated by hierarchical diffusion. Finally, this study draws conclusions on the temporal and spatial complexity of innovation network, which has a positive impact on quantifying spatial knowledge spillovers and measuring its space-time evolution. Besides, the results clarify the status of each city in innovation networks, which provides a new perspective for the cities to formulate innovative policies.

知识在产业集聚、区域创新中的地位越来越突出,城市知识储量及其在区域知识网络中的地位对城市的综合竞争力有重要影响。学术论文合作与专利合作是知识溢出的体现形式,是科学和技术发展的重要成果,也是度量区域创新能力的主要指标。以2000-2009年中国生物技术领域合著论文和共同申请专利的信息为原始数据,分别构建中国城市间科学知识网络(scientific knowledge network,SKN)与技术知识网络(technological knowledge network,TKN)。运用复杂网络与地学空间分析方法,从整体网络结构特征、择优链接性、中心城市及其自我网络的空间特征等方面进行分析,探究知识溢出的时空复杂性。研究表明：①SKN和TKN具有无标度网络特征;SKN节点度数的异质性高于TKN。②两种网络均呈异配性,即城市选择合作对象存在明显择优链接性,知识溢出具有粘着性和空间依赖性。③SKN中心城市具有明显的等级结构,空间分布总体呈“大分散小集聚”特点;TKN中心城市层级结构不明显,尚未形成明显极化中心。④SKN中心城市自我网络的合作空间,由最初的沿海省会城市间的合作转向长三角、珠三角、京津冀等区域间和沿海城市与内陆城市间的合作,区域间知识溢出明显;TKN中心城市自我网络仍多分布于沿海城市和少数中西部省会城市,区域间知识溢出不明显。⑤SKN中心城市及其自我网络的时空演变存在等级扩散和传染扩散的现象,符合时空梯度推移规律,且空间等级梯度逐渐向扁平化转变;TKN中心城市及其自我网络的时空演变以等级扩散为主,时空梯度推移现象不明显。研究结论为量化知识溢出及知识溢出网络结构的时空演化过程提供新的研究视角,对城市创新政策的制定有一定借鉴意义。

The concept of megalopolis, since its original inception six decades ago, has inspired many new terms that mainly describe large-scale urbanized forms such as megaregions and polycentric urban regions. However, recent studies have increasingly focused on the two key functions that megalopolises act as an incubator of new ideas and trends and as a hub that articulates knowledge exchange at the megalopolitan, national, and global scales. While the recent studies have mainly analyzed the functional aspects of megalopolis based on China's Yangtze River Delta region, this paper investigates the evolving process and mechanisms of knowledge collaboration within and beyond Guangdong-Hong Kong-Macao Greater Bay Area (GBA) - one of the most promising and vibrant megalopolises in China. In addition, the GBA megalopolis is unique because it contains Hong Kong and Macao, which have a different political system from China's mainland. Drawing upon a dataset of publications that were indexed in Web of Science Core Collection during the 1990-2016 period, this paper uses the Gini coefficient to measure the degree of knowledge polycentricity of the GBA megalopolis. Here, knowledge polycentricity is further classified into attribute polycentricity of knowledge production and functional polycentricity of knowledge collaboration within and beyond the GBA megalopolis. Whereas the attribute polycentricity refers to the distribution inequality of the total publications of GBA cities, the functional polycentricity represents the distribution inequality of GBA cities' knowledge collaboration at different geographical scales. Our empirical results show: (1) knowledge production of the GBA megalopolis as a whole has experienced a robust and continuous growth. The degrees of both attribute polycentricity and functional polycentricity have also been on the increase in general, although there are some fluctuations in early years and some deviations in recent years. During the ten years after Hong Kong and Macao returned to China (the 2000-2010 period), the degree of knowledge polycentricity of the GBA megalopolis especially enjoyed the fastest rise; (2) The degree of functional polycentricity decreased with the expansion in the geographical scales at which it is measured, confirming the findings of previous studies that functional polycentricity is scale-dependent. Moreover, we find that the degree of functional polycentricity becomes more fluctuated at the global scale while it tends to increase continuously at the megalopolitan scale; (3) The evolving process of knowledge polycentricity of the GBA megalopolis is influenced by institutional proximity, geographical proximity and status proximity between cities. Specifically, the mobility of researchers, the collaboration of universities and research institutes, and the coordination of local governments are three major forces promoting the evolution of knowledge polycentricity of the GBA megalopolis. Overall, the increasing knowledge polycentricity would be of significance for the GBA megalopolis to form a knowledge-driven region of collective collaboration.

基于1990-2016年“Web of Science”核心合集所收录的科研论文合著数据,借助基尼系数测度属性和功能多中心性的方法,对粤港澳大湾区城市群的知识多中心性及其知识网络的演化进行了研究。结果发现：① 伴随着粤港澳大湾区城市群知识生产总量的持续增长,其多中心性程度呈现出阶段性、阶梯式提升的特征,分别经历了波动、增长和分化的发展阶段,港澳回归后的2000-2010年间是多中心性快速增长的重要阶段。② 粤港澳大湾区城市群在区域、国家和全球尺度上的功能多中心性程度随着尺度增加逐级递减,进一步证实了功能多中心性的尺度规律性;而且发现了多中心在演化中的尺度敏感性,全球尺度上的多中心性会存在不确定的峰值,而区域尺度上的多中心性可能会持续增加。③ 城市群多中心的演化是受制度接近、地理接近和等级接近影响,在研究人员移动、科研单位联动和政府政策推动及其行动主体间的相互作用下实现的,多中心程度的增加有助于推动粤港澳大湾区城市群构建科研协同创新共同体。

On the basis of patent transaction data in 2015, spatial pattern of interurban technology transfer network in China was portrayed by integrating big data mining, social network, and GIS, from the perspectives of nodal strength and centrality, linkage intensity, and modular divisions. Then, its key influencing factors were identified as well using the Negative Binominal Regression Analysis. Some findings were ontained as follows. First of all, the intensity of interurban technology transfers in China is not well distributed with obvious polarization. Those cities with higher-level technology transfers are concentrated in the three urban clusters, namely, the Yangtze River Delta, the Pearl River Delta and Beijing-Tianjin-Hebei urban agglomeration. Secondly, a typical core-periphery structure with hub-and-spoke organization is evidently observed, which consists of several hubs and the majority of cities with far lower technology transfers. Beijing, Shenzhen, Shanghai and Guangzhou are acting as the pivot of the technology transfer network and playing a critical role in aggregating and dispersing technology flows. Thirdly, technology linkage intensities of urban pairs appear to be significantly uneven with hierarchies, centralizing in the three edges from Beijing to Shanghai, from Shanghai to Guangzhou and Shenzhen, and from Beijing to Guangzhou and Shenzhen, which shapes a triangle pattern. Fourthly, the technology transfer network is divided into four communities or plates, with prominent reflexivity and spillover effects, which is resulted from geographical proximity and technological complementary. Last but not least, spatial flows of technology are co-organized by a variety of spatial diffusion modes such as hierarchical diffusion, contact diffusion and leapfrog diffusion, owing to economic and administrative powers. They are greatly influenced by urban economic scale, foreign linkage, policy making, as well as multiple proximity factors related to geographical, technological, social and industrial proximities.

基于2015年专利交易数据,融合数据挖掘、社会网络、空间分析等方法,从节点、关联、模块及影响因素4个方面揭示中国城际技术转移的空间格局及其影响因素：① 技术转移整体强度偏低,空间极化严重,长三角、珠三角、京津冀城市群成为技术转移的活跃地带。② 北京、深圳、上海、广州是全国技术转移网络的“集线器”,发挥城际技术流的集散枢纽和中转桥梁作用,中西部大部分城市处于网络边缘,整个网络发育典型的核心—边缘式和枢纽—网络式结构。③ 技术关联的空间层级和马太效应凸显,形成以北京、上海、广深为顶点的“三角形”技术关联骨架结构,技术流集聚在东部地带经济发达的城市之间和具有高技术能级的城市之间,中西部技术结网不足,呈现碎片化。④ 技术转移网络形成明显的四类板块（子群）,具明显自反性和溢出效应,其空间聚类既有“近水楼台先得月”式块状集聚,也有“舍近求远”式点状“飞地”镶嵌。⑤ 城际技术流呈现等级扩散、接触扩散、跳跃扩散等多种空间扩散模式,其流向表现出经济指向性和行政等级指向性特征。⑥ 城市经济发展水平、对外开放程度、政策支持等主体属性和地理、技术、社会、产业邻近性的城市主体关系均会影响其技术转移强度。

The spatial structure characteristics and influencing factors of knowledge networks are heated research topics in the field of economic geography. Previous studies in knowledge networks have focused on industrial cases, especially on knowledge networks at the national and city levels, while the research of individuals has been relatively limited. We firstly selected highly cited Chinese scientists based on 2014-2015 global highly cited scientists as the original data, and then established knowledge network based on the co-authorship of papers among those scientists, and finally applied social network analysis methods to examine the spatial structure of the knowledge network and used the negative binomial regression model to explore the influence mechanism of knowledge network from the perspectives of geographic proximity, social proximity and institutional proximity. The study found that: (1) the knowledge network of highly cited Chinese scientists has core-peripheral structure with the characteristics of a small-world network. (2) The spatial distribution of this knowledge network is dispersal at global scale and agglomerated at local level. (3) The formation of the knowledge network is influenced by the personal characters of those scientists. Geographical distance has a negative effect on the connection between scientists. The influence of geographic proximity still exists. Both social proximity and institutional proximity have significant positive effects on the formation of knowledge networks of highly cited Chinese scientists.

知识网络的空间结构特征与影响因素是经济地理学探讨的热点议题之一,以往研究侧重于产业案例的分析,主要关注国家和城市层面的知识网络,而对科学家等个人层面的网络研究较为缺乏。以2014—2015年全球高被引科学家为原始数据,筛选出高被引华人科学家,并基于Web of Science数据库,检索高被引华人科学家之间合著论文的数据构建知识网络,借助社会网络分析方法对高被引华人科学家知识网络的空间结构进行分析;并运用负二项回归模型,从地理邻近性、社会邻近性、制度邻近性3个维度,探讨高被引华人科学家知识网络的影响机制。研究发现：① 高被引华人科学家知识网络存在核心-边缘结构特征,且具有小世界网络的网络特征;② 此知识网络呈现“小集聚大分散”的空间结构特征,地理邻近性作用明显;③ 高被引华人科学家知识网络形成过程中会受到科学家自身科研能力等因素的影响,地理距离和科学家之间的联系呈现负相关关系,地理邻近性的影响仍然存在,社会邻近性和制度邻近性均对知识网络的形成有促进作用。

Despite increasing importance of academic papers in global knowledge flows, the structural disparities and proximity mechanism related to international scientific collaboration network attracted little attention. To fill this gap, based on data mining from Thomson Reuters' Web of Science database in 2014, its heterogeneities in topology and space were portrayed using visualizing tools such as Pajek, Gephi, VOSviewer, and ArcGIS. Topologically, 211 countries and 9928 ties are involved in global scientific collaboration network, but the international network of co-authored relations is mono-centricand dominated by the United States. It exhibits some features of a "small-world" network with the smaller average path length of 1.56 and the extremely large cluster coefficient of 0.73 compared to its counterpart, as well as the better-fitting exponential distribution accumulative nodal degree. In addition, the entire network presents a core-periphery structure with hierarchies, which is composed of 13 core countries and the periphery of 198 countries. Spatially, densely-tied and high-output areas are mainly distributed in four regions: West Europe, North America, East Asia and Australia. Moreover, the spatial heterogeneity is also observed in the distributions of three centralities. Amongst these, the countries with greater strength centrality are mainly concentrated in North America (i.e. the US and Canada), Western Europe (i.e. the UK, France, Germany, Italy and Spain), and China, noticeably in the US, which forms the polarizing pattern with one superpower of the US and great powers such as China and the UK. Similarly, the big three regions consisting of West Europe, North America and Asian-Pacific region have the peak betweenness centrality as well. Slightly different from the two above, the distribution of nodal degree centrality is uneven in the world, although regional agglomeration of high-degree countries is still observed. Last but not least, the proximity factors of its structural inequalities were also verified by correlational analysis, negative binomial regression approach and gravity model of STATA. The findings further confirm that geographical distance has weakened cross-country scientific collaboration. Meanwhile, socio-economic proximity has a positive impact on cross-country scientific collaboration, while language proximity plays a negative role.

以科研论文为媒介的知识合作网络已成为知识溢出的重要通道,但目前学术界对全球科研合作网络结构的复杂性涌现机制缺乏深入的探讨。基于2014年Web of Science核心合集所收录的科研论文合著数据,借助大数据挖掘技术、复杂网络、空间统计和重力模型分析,刻画了全球科研论文合作网络的拓扑结构、空间格局及其邻近性机理。结果发现：① 拓扑结构上,形成了以美国为核心的层级网络,具有小世界性和等级层次性,发育出典型的等级“核心—边缘”结构。② 空间格局上,以美国、西欧、中国和澳大利亚为顶点的“四边形”成为全球科研论文合作网络的骨架;三大中心性指标值的空间分异明显,强度中心性形成以美国为极核,加拿大、澳大利亚、中国及西欧诸国为次中心的“一超多强”格局,与之类似的介数中心性呈现北美、西欧和东亚“三足鼎立”的形态,度中心性分布则相对均匀,表现出“大分散、小集中”的“多中心—边缘集散”格局。③ 重力回归分析发现,地理距离抑制了国际科研论文合作,不过其影响力较弱;社会与经济邻近性对全球科研论文合作具有明显的促进作用,语言差异不是国际科研合作交流的障碍。

Human brain functional networks are embedded in anatomical space and have topological properties--small-worldness, modularity, fat-tailed degree distributions--that are comparable to many other complex networks. Although a sophisticated set of measures is available to describe the topology of brain networks, the selection pressures that drive their formation remain largely unknown. Here we consider generative models for the probability of a functional connection (an edge) between two cortical regions (nodes) separated by some Euclidean distance in anatomical space. In particular, we propose a model in which the embedded topology of brain networks emerges from two competing factors: a distance penalty based on the cost of maintaining long-range connections; and a topological term that favors links between regions sharing similar input. We show that, together, these two biologically plausible factors are sufficient to capture an impressive range of topological properties of functional brain networks. Model parameters estimated in one set of functional MRI (fMRI) data on normal volunteers provided a good fit to networks estimated in a second independent sample of fMRI data. Furthermore, slightly detuned model parameters also generated a reasonable simulation of the abnormal properties of brain functional networks in people with schizophrenia. We therefore anticipate that many aspects of brain network organization, in health and disease, may be parsimoniously explained by an economical clustering rule for the probability of functional connectivity between different brain areas.

Exponential-family random graph models (ERGMs) provide a principled and flexible way to model and simulate features common in social networks, such as propensities for homophily, mutuality, and friend-of-a-friend triad closure, through choice of model terms (sufficient statistics). However, those ERGMs modeling the more complex features have, to date, been limited to binary data: presence or absence of ties. Thus, analysis of valued networks, such as those where counts, measurements, or ranks are observed, has necessitated dichotomizing them, losing information and introducing biases. In this work, we generalize ERGMs to valued networks. Focusing on modeling counts, we formulate an ERGM for networks whose ties are counts and discuss issues that arise when moving beyond the binary case. We introduce model terms that generalize and model common social network features for such data and apply these methods to a network dataset whose values are counts of interactions.

Based on the data released by UNESCO on international student mobility in tertiary education from 2001 to 2015, this paper draws on network science to construct a multidimensional weighted directed network heterogeneity model. This model combines the GIS spatial analysis method and the negative binomial regression model to study spatiotemporal evolution and factors influencing international student mobility networks. The results are as follows. First, in terms of the evolutionary characteristics of time series, the linkages of international student mobility networks have increased from 4921 to 9137, and its average weighted degree centrality has rapidly increased from 8004 to 20,834, which indicates that both the number of international students and the choices of overseas routines are mounting. Second, in terms of the topological structure, it has evolved from a single core comprising the United States into dual cores comprising the United States and China. Gradually, Asian countries have become the main body of international student mobility networks, while the centrality of European countries has reduced. Thirdly, in terms of spatial structure, the international student mobility network has a significant core-periphery structure and hierarchical characteristics. The countries in the core, strong semi-periphery and semi-periphery alternate with countries from other tiers. The spatial patterns of the international student mobility network "from east to west, and from south to north" are changing. China and Australia have become the new regional centers of international student mobility networks, meaning that regionalization has become an important trend. Finally, regression analysis shows that the flow of international students correlates positively with the quality of higher education in destination countries, the number of students in colleges and universities, the per capita GDP, the historical links connection between the destination country and the country of origin, and the level of globalization. Geographical distance and linguistic proximity have a significant negative correlation with the flow of international students, indicating that the impediment of language is rapidly weakening, while geographical distance still hinders student mobility.

基于联合国教科文组织公布的全球高等学校留学生流动数据,借鉴网络科学,利用社会网络分析方法和负二项回归模型,对2001—2015年全球留学网络时空演化及其影响因素进行研究。主要结论为：① 时序演化特征上,全球留学网络关系流从4921个增加到9137个,平均加权度从8004迅速增加到20834,表明全球留学网络不仅学生数量在增加,且留学路径数量也在不断增加。② 拓扑结构上,从以美国为单一核心,演变成以美国、中国为双核心的拓扑结构,亚洲国家逐渐成为全球留学网络的主体,欧洲国家的中心性逐渐萎缩。③ 空间结构上,全球留学网络具有明显的等级层次性,美国是全球性主导型节点,德国、捷克是区域性主导型节点,次级主导型节点从25个增加到32个,全球留学网络“东向西、南到北”的地理空间格局正在发生变化,中国、澳大利亚等国成为新的区域性中心,区域化成为全球留学网络一个重要趋势。④ 回归分析发现,留学目的地国高等教育质量、高校在校生数量、人均GDP、留学目的地国与留学生来源国间的历史联系及全球化水平与留学生流量呈显著正相关关系,地理距离、语言临近性与留学生流量呈显著负相关关系,表明语言差异的阻抗作用在迅速减弱,地理距离仍然是阻碍学生流动的一个重要因素。

China proposed the Belt and Road Initiative (BRI) to facilitate the economic development of the Belt and Road (B&R) region. In the vision of BRI, countries will utilize their comparative advantages to foster cultural and educational exchanges, build scientific and technological platforms, enhance relevant institution for long-term and stable scientific and technological collaboration, and elevate innovation abilities. As a result, B&R technological transfer networks can be constructed. At the same time, the integration of regional innovation can be achieved. However, within the B&R region, except a few developed countries, most countries are still developing or less developed. These countries sometimes show quite different evolutionary mechanism from the developed ones. Therefore, research on B&R technical corporation networks has both pragmatic and theoretical meanings in boosting the development of B&R countries. Under the background of knowledge economy, technology innovation becomes the key to regional economic development. Cities are the main platform of technology activities. So in order to explore the technology activities in and outside B&R region, we investigate the PCT patent data from 2007 to 2018. The inner and outer B&R technical corporation networks are constructed to present the topological structure and spatial distribution of the technical corporation activities. The negative binomial regression is used to detect the dynamic mechanism from the perspective of the proximity theory. The results show that in terms of topological structures, the networks evolve from Scale Extension to Linkage Enhancement stage. Chinese cities gradually reach the core position. In terms of spatial distribution, most linkages between B&R cities are domestic. Meanwhile, cities from the same country show similar pattern of choosing outer corporation partners. Singapore is the most important hub in both inner and outer B&R networks. We find that the mass of cities has significant impact on corporation. Geographical proximity and economic proximity have the significant negative impact, while social proximity, technology proximity and language proximity have the significant positive impact. In addition, geographical proximity and economic proximity have complementary effects as well as geographical proximity and language proximity.

采用2007—2018年的PCT专利合作数据,基于“一带一路”视角,分段刻画城市技术合作网络的拓扑结构、空间格局及其时空演化,利用负二项回归方法分析其演化的邻近性机理,结果表明：① 拓扑结构方面,网络整体规模经历了从“规模扩大”到“联系增强”的演化过程,中国城市逐渐占据网络的核心层级。② 空间格局方面,“一带一路”城市之间的合作多为国家内部合作,来自同一国家的城市在选择外部合作伙伴时具有一定的相似性。新加坡是“一带一路”内部网络和对外网络的枢纽节点。③ 影响机制方面,合作城市的质量对合作具有显著正向作用,地理邻近性和经济邻近性具有显著负向作用,社会邻近性、认知邻近性和语言邻近性具有显著正向作用,而地理邻近性与经济邻近性、地理邻近性与语言邻近性具有相互补充作用。

This paper presents a novel approach to investigating and understanding the evolving structure of the Southeast Asian air transport network (SAAN) over the period 1979-2012. Our approach captures the main topological and spatial changes from a complex network perspective. We find that the SAAN combines a relatively stable topological structure with a changing multilayered geographical structure. Statistical analysis indicates that the SAAN is a scale-free network with an increasing number of hub cities and has been characterized by small-world properties since 1996. Furthermore, the SAAN exhibits a recently intensified disassortative mixing pattern, suggesting an increasing dependence of small cities on hub-and-spoke configuration for better accessibility. A decomposition analysis is used to disaggregate the SAAN into a hierarchical core-bridge-periphery structure. The core layer consists of capital cities, the most economic vibrant secondary cities, and tourist destinations. This core layer is also densely interconnected with its center of gravity moving towards the north. The periphery layer, comprised of cities in remote areas, sustains a low significance with declining internal connectivity despite a rising number of cities being connected. The bridge layer lies in between both extremes, and is characterized by a high volatility over time. The connections and passengers between different layers increase, especially those between core and bridge after 1996. In our discussion, we trace these changes back to a series of socio-economic and politico-institutional dynamics in Southeast Asia.© 2018 Elsevier Ltd. All rights reserved.

Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.