A new evolutional model for institutional field knowledge flow network

Purpose:This paper aims to address the limitations in existing research on the evolution of knowledge flow networks by proposing a meso-level institutional field knowledge flow network evolution model(IKM).The purpose is to simulate the construction process of a knowledge flow network using knowledge organizations as units and to investigate its effectiveness in replicating institutional field knowledge flow networks.Design/Methodology/Approach:The IKM model enhances the preferential attachment and growth observed in scale-free BA networks,while incorporating three adjustment parameters to simulate the selection of connection targets and the types of nodes involved in the network evolution process Using the PageRank algorithm to calculate the significance of nodes within the knowledge flow network.To compare its performance,the BA and DMS models are also employed for simulating the network.Pearson coefficient analysis is conducted on the simulated networks generated by the IKM,BA and DMS models,as well as on the actual network.Findings:The research findings demonstrate that the IKM model outperforms the BA and DMS models in replicating the institutional field knowledge flow network.It provides comprehensive insights into the evolution mechanism of knowledge flow networks in the scientific research realm.The model also exhibits potential applicability to other knowledge networks that involve knowledge organizations as node units.Research Limitations:This study has some limitations.Firstly,it primarily focuses on the evolution of knowledge flow networks within the field of physics,neglecting other fields.Additionally,the analysis is based on a specific set of data,which may limit the generalizability of the findings.Future research could address these limitations by exploring knowledge flow networks in diverse fields and utilizing broader datasets.Practical Implications:The proposed IKM model offers practical implications for the construction and analysis of knowledge flow networks within institutions.It provides a valuable tool for understanding and managing knowledge exchange between knowledge organizations.The model can aid in optimizing knowledge flow and enhancing collaboration within organizations.Originality/value:This research highlights the significance of meso-level studies in understanding knowledge organization and its impact on knowledge flow networks.The IKM model demonstrates its effectiveness in replicating institutional field knowledge flow networks and offers practical implications for knowledge management in institutions.Moreover,the model has the potential to be applied to other knowledge networks,which are formed by knowledge organizations as node units.

Knowledge flow analysis of knowledge co-production-based climate change adaptation for lowland rice farmers in Bulukumba Regency,Indonesia

To increase the resilience of farmers’livelihood systems,detailed knowledge of adaptation strategies for dealing with the impacts of climate change is required.Knowledge co-production approach is an adaptation strategy that is considered appropriate in the context of the increasing frequency of disasters caused by climate change.Previous research of knowledge co-production on climate change adaptation in Indonesia is insufficient,particularly at local level,so we examined the flow of climate change adaptation knowledge in the knowledge co-production process through climate field school(CFS)activities in this study.We interviewed 120 people living in Bulukumba Regency,South Sulawesi Province,Indonesia,involving 12 crowds including male and female farmers participated in CFS and not participated in CFS,local government officials,agriculture extension workers,agricultural traders,farmers’family members and neighbors,etc.In brief,the 12 groups of people mainly include two categories of people,i.e.,people involved in CFS activities and outside CFS.We applied descriptive method and Social network analysis(SNA)to determine how knowledge flow in the community network and which groups of actors are important for knowledge flow.The findings of this study reveal that participants in CFS activities convey the knowledge they acquired formally(i.e.,from TV,radio,government,etc.)and informally(i.e.,from market,friends,relatives,etc.)to other actors,especially to their families and neighbors.The results also show that the acquisition and sharing of knowledge facilitate the flow of climate change adaptation knowledge based on knowledge co-operation.In addition,the findings highlight the key role of actors in the knowledge transfer process,and key actors involved in disseminating information about climate change adaptation.To be specific,among all the actors,family member and neighbor of CFS actor are the most common actors in disseminating climate knowledge information and closest to other actors in the network;agricultural trader and family member of CFS actor collaborate most with other actors in the community network;and farmers participated in CFS,including those heads of farmer groups,agricultural extension workers,and local government officials are more willing to contact with other actors in the network.To facilitate the flow of knowledge on climate change adaptation,CFS activities should be conducted regularly and CFS models that fit the situation of farmers’vulnerability to climate change should be developed.

Research on Cognitive Cooperation in Multi-Agent Systems

The accomplishment of a complex problem usually involves cooperation between participators with different knowledge background concerned. This paper identifies interdependency between different sub problems （through problem decomposition） as the major factor that influences cooperative relations in multi-Agent systems, based on which we propose an efficient means to measure cooperation coefficient （degree） between different Agents. Then cognitive cooperation between Agents is analyzed which aims at collecting the wisdom of the cognitive community for a systematic solution to the overall problem.

中国城市间科学合作网络空间的层级性、集聚性与差异性分析（英文）

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 subregional 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.

Discovering the communication structures of directed weighted citation networks in library and information science through Pathfinder

Communication structures mining is of importance for the understanding of the communities of a domain and knowledge flow among papers and authors.In this paper,we take advantage of Pathfinder,a method for pruning networks,to discover the communities of a directed weighted citation network and its main knowledge flow structure.Meanwhile,in the course of the analysis,necessary data transformations are carried out,and proper parameters for Pathfinder are determined.It is found that Pathfinder plays a multifaceted role in the discovery of communication structures of directed weighted citation networks,which could provide more systematic insights to citation network analytics.