Fractal Analysis of Mobile Social Networks

Fractal and self similarity of complex networks have attracted much attention in recent years. The fractal dimension is a useful method to describe the fractal property of networks. However, the fractal features of mobile social networks （MSNs） are inadequately investigated. In this work, a box-covering method based on the ratio of excluded mass to closeness centrality is presented to investigate the fractal feature of MSNs. Using this method, we find that some MSNs are fractal at different time intervals. Our simulation results indicate that the proposed method is available for analyzing the fractal property of MSNs.

A comparative study of information diffusion in weblogs and microblogs based on social network analysis

Purpose: This paper intends to explore a quantitative method for investigating the characteristics of information diffusion through social media like weblogs and microblogs.By using the social network analysis methods,we attempt to analyze the different characteristics of information diffusion in weblogs and microblogs as well as the possible reasons of these differences.Design/methodology/approach: Using the social network analysis methods,this paper carries out an empirical study by taking the Chinese weblogs and microblogs in the field of Library and Information Science(LIS) as the research sample and employing measures such as network density,core/peripheral structure and centrality.Findings: Firstly,both bloggers and microbloggers maintain weak ties,and both of their social networks display a small-world effect. Secondly,compared with weblog users,microblog users are more interconnected,more equal and more capable of developing relationships with people outside their own social networks. Thirdly,the microblogging social network is more conducive to information diffusion than the blogging network,because of their differences in functions and the information flow mechanism. Finally,the communication mode emerged with microblogging,with the characteristics of micro-content,multi-channel information dissemination,dense and decentralized social network and content aggregation,will be one of the trends in the development of the information exchange platform in the future.Research limitations: The sample size needs to be increased so that samples are more representative. Errors may exist during the data collection. Moreover,the individual-level characteristics of the samples as well as the types of information exchanged need to be further studied.Practical implications: This preliminary study explores the characteristics of information diffusion in the network environment and verifies the feasibility of conducting a quantitative analysis of information diffusion through social media. In addition,it provides insight into the characteristics of information diffusion in weblogs and microblogs and the possible reasons of these differences.Originality/value: We have analyzed the characteristics of information diffusion in weblogs and microblogs by using the social network analysis methods. This research will be useful for a quantitative analysis of the underlying mechanisms of information flow through social media in the network environment.

Evaluations on social network analysis (SNA) research in China's Mainland:A critical assessment of journal literatures

Social network analysis(SNA) has been introduced to China's Mainland since the end of last century. It is often stated that SNA research has experienced rapid growth in China over these years, but few studies have been conducted to prove the statement. This paper aims at exploring the research status and development of SNA in China by a critical assessment of journal articles. Our findings show that SNA is an evolving and diversified research area which has rich themes and topics, and can be applied to those studies on different levels, context and disciplines, and attract researchers and scholars from various fields and domains. In addition, the information community(Library & Information Science and Information Systems) plays a leading role in the SNA related researches. The paper also points out the research on SNA in China has some limitations, so it proposes several implications for the future development of SNA research from perspectives of information science.

Model for Generating Scale-Free Artificial Social Networks Using Small-World Networks

The Internet of Things(IoT)has the potential to be applied to social networks due to innovative characteristics and sophisticated solutions that challenge traditional uses.Social network analysis(SNA)is a good example that has recently gained a lot of scientific attention.It has its roots in social and economic research,as well as the evaluation of network science,such as graph theory.Scientists in this area have subverted predefined theories,offering revolutionary ones regarding interconnected networks,and they have highlighted the mystery of six degrees of separation with confirmation of the small-world phenomenon.The motivation of this study is to understand and capture the clustering properties of large networks and social networks.We present a network growth model in this paper and build a scale-free artificial social network with controllable clustering coefficients.The random walk technique is paired with a triangle generating scheme in our proposed model.As a result,the clustering controlmechanism and preferential attachment(PA)have been realized.This research builds on the present random walk model.We took numerous measurements for validation,including degree behavior and the measure of clustering decay in terms of node degree,among other things.Finally,we conclude that our suggested random walk model is more efficient and accurate than previous state-of-the-art methods,and hence it could be a viable alternative for societal evolution.

Content analysis of documents using neural networks: A study of Antarctic science research articles published in international journals

Content analysis of scientific papers emanating from Antarctic science research during the 25 years period （1980-- 2004） has been carried out using neural network based algorithm-CATPAC. A total of 10 942 research articles published in Science Citation Indexed （SCI） journals were used for the study. Normalized co-word matrix from 35 most-used significant words was used to study the semantic association between the words. Structural Equivalence blocks were constructed from these 35 most-used words. Four-block model solution was found to be optimum. The density table was dichotomized using the mean density of the table to derive the binary matrix, which was used to construct the network map. Network maps represent the thematic character of the blocks. The blocks showed preferred connection in establishing semantic relationship with the blocks, characterizing thematic composition of Antarctic science research. The analysis has provided an analytical framework for carrying out studies on the con- tent of scientific articles. The paper has shown the utility of co-word analysis in highlighting the important areas of research in Antarctic science.

Advanced Community Identification Model for Social Networks

Community detection in social networks is a hard problem because of the size,and the need of a deep understanding of network structure and functions.While several methods with significant effort in this direction have been devised,an outstanding open problem is the unknown number of communities,it is generally believed that the role of influential nodes that are surrounded by neighbors is very important.In addition,the similarity among nodes inside the same cluster is greater than among nodes from other clusters.Lately,the global and local methods of community detection have been getting more attention.Therefore,in this study,we propose an advanced communitydetection model for social networks in order to identify network communities based on global and local information.Our proposed model initially detects the most influential nodes by using an Eigen score then performs local expansion powered by label propagation.This process is conducted with the same color till nodes reach maximum similarity.Finally,the communities are formed,and a clear community graph is displayed to the user.Our proposed model is completely parameter-free,and therefore,no prior information is required,such as the number of communities,etc.We perform simulations and experiments using well-known synthetic and real network benchmarks,and compare them with well-known state-of-the-art models.The results prove that our model is efficient in all aspects,because it quickly identifies communities in the network.Moreover,it can easily be used for friendship recommendations or in business recommendation systems.

Research Hotspots and Trends Analysis of Real-World Data Based on Social Network Analysis and Knowledge Graph

Objective To study the research status,research hotspots and development trends in the field of real-world data(RWD)through social network analysis and knowledge graph analysis.Methods RWD of the past 10 years were retrieved,and literature metrological analysis was made by using UCINET and CiteSpace from CNKI.Results and Conclusion The frequency and centrality of related keywords such as real-world study,hospital information system(HIS),drug combination,data mining and TCM are high.The clusters labeled as clinical medication and RWD contain more keywords.In recent 4 years,there are more articles involving the keywords of data specification,data authenticity,data security and information security.Among them,compound Kushen injection,HIS database and RWD are the top three keywords.It is a long-term research hotspot for Chinese and western medicine to use HIS to study clinical medication,clinical characteristics,diseases and injections.Besides,the research of RWD database has changed from construction to standardized collection and governance,which can make RWD effective.Data authenticity,data security and information security will become the new hotspots in the research of RWD.

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.

Local Trade Networks among Farmers and Traders

Both farmers and traders benefit from trade networking, which is crucial for the local economy. Therefore, it is crucial to understand how these networks operate, and how they can be managed more effectively. Throughout this study, we examine the economic networks formed between farmers and traders through the trade of food products. These networks are analyzed from the perspective of their structure and the factors that influence their development. Using data from 18 farmers and 15 traders, we applied exponential random graph models. The results of our study showed that connectivity, Popularity Spread, activity spread, good transportation systems, and high yields all affected the development of networks. Therefore, farmers’ productivity and high market demand can contribute to local food-crop trade. The network was not affected by reciprocity, open markets, proximity to locations, or trade experience of actors. Policy makers should consider these five factors when formulating policies for local food-crop trade. Additionally, local actors should be encouraged to use these factors to improve their network development. However, it is important to note that these factors alone cannot guarantee success. Policy makers and actors must also consider other factors such as legal frameworks, economic policies, and resource availability. Our approach can be used in future research to determine how traders and farmers can enhance productivity and profit in West Africa. This study addresses a research gap by examining factors influencing local food trade in a developing country.

Industrial Carbon Emission Distribution and Regional Joint Emission Reduction:A Case Study of Cities in the Pearl River Basin,China

China’s low-carbon development path will make significant contributions to achieving global sustainable development goals.Due to the diverse natural and economic conditions across different regions in China,there exists an imbalance in the distribution of car-bon emissions.Therefore,regional cooperation serves as an effective means to attain low-carbon development.This study examined the pattern of carbon emissions and proposed a potential joint emission reduction strategy by utilizing the industrial carbon emission intens-ity(ICEI)as a crucial factor.We utilized social network analysis and Local Indicators of Spatial Association(LISA)space-time trans-ition matrix to investigate the spatiotemporal connections and discrepancies of ICEI in the cities of the Pearl River Basin(PRB),China from 2010 to 2020.The primary drivers of the ICEI were determined through geographical detectors and multi-scale geographically weighted regression.The results were as follows:1)the overall ICEI in the Pearl River Basin is showing a downward trend,and there is a significant spatial imbalance.2)There are numerous network connections between cities regarding the ICEI,but the network structure is relatively fragile and unstable.3)Economically developed cities such as Guangzhou,Foshan,and Dongguan are in the center of the network while playing an intermediary role.4)Energy consumption,industrialization,per capita GDP,urbanization,science and techno-logy,and productivity are found to be the most influential variables in the spatial differentiation of ICEI,and their combination in-creased the explanatory power of the geographic variation of ICEI.Finally,through the analysis of differences and connections in urban carbon emissions under different economic levels and ICEI,the study suggests joint carbon reduction strategies,which are centered on carbon transfer,financial support,and technological assistance among cities.

Evaluation of social network conservation in historical areas of mountainous towns in Chongqing, China

Over recent decades,historical areas conservation has become an important strategy to improve urban competitiveness in the global economy.As shown in existing studies that the conservation of historical areas mainly focused on the physical environment,there is still room for the non-physical study,and researches on the social network conservation in mountainous historical areas are particularly insufficient.Therefore,this paper aims to establish an evaluation system which is helpful for the social network conservation of historical areas.The evaluation system is based on social network analysis and the information of social relationships gathered in field surveys using a specifically designed questionnaire method in four mountainous towns in Chongqing,China.And it was analyzed from three perspectives,i.e.,by the basic statistical properties,condensate subgroup,and centrality.Then five analysis indicators were conceived,including density,lambda set,k-core,degree centrality,and betweenness centrality.The analysis results demonstrate that the social networks of the four towns show different indicators,which are respectively relevant to completeness degree,edgerelatedness level,local stability,structural balance,and concentrated trend of social relationships.Results from SNA modeling indicate that neighborhood residents of historical areas who have more stable and healthier social relationships are relatively not easily be destroyed.The results also illustrate that the social networks structure is influenced by the terrain,form,and function of historical areas,and the change of historical areas is caused by"individual-family-society".Finally,the strategies guiding the social network conservation are put forward from two aspects.These findings suggest that the conservation and management of social network and aborigines in historical areas should be emphasized to increase the collective benefits and vitality.

Exploring tourism networks in the Guangxi mountainous area using mobility data from user generated content

Tourism-led economic growth and tourism-driven urbanization have attracted increasing attention by provinces and regions in China with abundant tourism resources.Due to low data availability,the current tourism literature lacks empirical evidence of the tourism network in lessdeveloped mountainous regions where the development of transport infrastructure is more variable.This paper aims to provide such evidence using Guangxi Zhuang Autonomous Region in China as a case study.Using User Generated Content(UGC)data,this study constructs a tourism network in Guangxi.By integrating social network analysis with spatial interaction modelling,we compared the impact of two different transport infrastructures,highway and high-speed railway,on tourist flows,particularly in less-developed mountainous regions.It was found that the product of node centrality and flow could best describe the significant pushing and pulling forces on the flow of tourists.The tourism by high-speed railway was sensitive to the position of trip destination on the whole tourism network but self-drive tourism was more sensitive to travelling time.The increase of high-speed railway density is crucial to promote local tourism-led economic development,however,large-scale karst landforms in the study area present a significant obstacle to the construction of high-speed railways.

Global Collaboration in Artificial Intelligence:Bibliometrics and Network Analysis from 1985 to 2019

Purpose:This study aims to explore the trend and status of international collaboration in the field of artificial intelligence(AI)and to understand the hot topics,core groups,and major collaboration patterns in global AI research.Design/methodology/approach:We selected 38,224 papers in the field of AI from 1985 to 2019 in the core collection database of Web of Science(WoS)and studied international collaboration from the perspectives of authors,institutions,and countries through bibliometric analysis and social network analysis.Findings:The bibliometric results show that in the field of AI,the number of published papers is increasing every year,and 84.8%of them are cooperative papers.Collaboration with more than three authors,collaboration between two countries and collaboration within institutions are the three main levels of collaboration patterns.Through social network analysis,this study found that the US,the UK,France,and Spain led global collaboration research in the field of AI at the country level,while Vietnam,Saudi Arabia,and United Arab Emirates had a high degree of international participation.Collaboration at the institution level reflects obvious regional and economic characteristics.There are the Developing Countries Institution Collaboration Group led by Iran,China,and Vietnam,as well as the Developed Countries Institution Collaboration Group led by the US,Canada,the UK.Also,the Chinese Academy of Sciences(China)plays an important,pivotal role in connecting the these institutional collaboration groups.Research limitations:First,participant contributions in international collaboration may have varied,but in our research they are viewed equally when building collaboration networks.Second,although the edge weight in the collaboration network is considered,it is only used to help reduce the network and does not reflect the strength of collaboration.Practical implications:The findings fill the current shortage of research on international collaboration in AI.They will help inform scientists and policy makers about the future of AI research.Originality/value:This work is the longest to date regarding international collaboration in the field of AI.This research explores the evolution,future trends,and major collaboration patterns of international collaboration in the field of AI over the past 35 years.It also reveals the leading countries,core groups,and characteristics of collaboration in the field of AI.

Evaluation and optimization analysis of high-speed rail network structure in Northeast China under the background of northeast revitalization

The construction of high-speed rail(HSR)network has promoted the social-economic ties of cities,accelerated the compression of time and space,and changed the pattern of regional development.In this paper,with the adoption of the operation frequency data of HSR from 12306 website,and based on the HSR connection strength model and social network analysis model,as well as according to the HSR connection strength,HSR network density,centrality,agglomeration subgroup,and other indicators,we analyzed the characteristics of HSR network structure in Northeast China.Results show that the number of HSR cities in Northeast China is small,cities in HSR network generally exhibit weak connectivity,and the existence of HSR network marginalizes cities such as Ulanhot,Baicheng,and Songyuan,which significantly reduce the overall network connectivity of Northeast China.The overall centrality of HSR network in Northeast China is characterized by“one axis,four edges”;specifically,the one axis is located in Harbin-Dalian transportation line and the four edges are located on both sides of the main axis of Harbin-Dalian transportation line.Eight agglomeration subgroups(four double city subgroups and four multi city subgroups)have formed in Northeast China.The core status of Shenyang in HSR network is improved significantly,and“one axis and two wings”HSR network in Liaoning Province is improved significantly.With the gradual expansion of Chaoyang-Fuxin,Dandong-Benxi,and Jilin-Yanji branch networks,the“point axis”HSR network mode in Northeast China has gradually developed and matured.In the future,it is recommended to rely on eight agglomerating subgroups to encrypt HSR network structure,create secondary node central cities,and gradually build a new pattern of opening up in Northeast China.

Interpreting Nestedness and Modularity Structures in Affiliation Networks: An Application in Knowledge Networks Formed by Software Project Teams

An understanding of the knowledge creation and diffusion process in the organizational context is extremely relevant. Because from this understanding, organizations can restructure processes, reorient teams and implement methodologies to assist in the construction of an evolutionary process of knowledge creation and diffusion aimed at sustainable growth and innovation. The theory of complex social networks has been applied in several fields to help understand organizational cognitive processes. However, these approaches still insipiently consider the analysis of the nestedness and modularity of the studied networks. In this article, we presented an approach that sought to identify patterns of nestedness and modularity in networks of affiliation of people in projects in the organizational context. The study sought to identify these patterns in affiliation networks in a public organization providing information technology services in the period from 2006 to 2013. The detection of these patterns was performed using the NODF (Nestedness metric based on Overlap and Decreasing Fill) algorithm described by [1]. The nestedness and modularity metrics can influence patterns of knowledge creation and diffusion in formal and informal networks constituted for the execution of projects in organizations. This study showed that the network structures of the organization during the study period presented a high degree of nestedness, and it was possible to identify combined structures of nestedness and modularity.

Dynamics of Knowledge in Software Project Development Environments: An Approach Using Affiliation Networks

This work presents an approach to study the diffusion of knowledge in software development project teams based on the formation of complex social network structures in a public organization that offers information and communication technology services. We collected historical data on the allocation and records of hours worked by people in projects to build an affiliation network. We applied the method of reflections to analyze the data obtained. The constructed model enabled the description of the participation of project team members from the perspective of the creation and diffusion of knowledge in affiliation networks that describe the participation of people in projects, mediated by the knowledge and the capabilities developed for the execution of these projects. A contribution of this work is the construction of indicators related to the process of creation and diffusion of knowledge in the context of the execution of software development and maintenance projects, based on the concepts of diversification and ubiquity applied to the process of knowledge diffusion;an additional contribution is the presentation of an application of the method of reflections in an organizational context applied to the creation and diffusion of knowledge. We found that the application of management models associated with the collaborative method applied to the project development process contributed to the joint growth of diversified and more specialized knowledge alongside the knowledge considered more generic and ubiquitous. Our results show that contrary to previous expectations based on assumptions established at the beginning of the study, we concluded that in six of the seven subnetworks obtained in the period between 2007 and 2013, knowledge based on complex and diversified capabilities showed a growth proportional to the growth of knowledge related to the capabilities necessary to more general and ubiquitous activities.

Bibliometric analysis of subject trends and knowledge structures of gut microbiota

BACKGROUND Gut microbiota is an emerging field of research,with related research having breakthrough development in the past 15 years.Bibliometric analysis can be applied to analyze the evolutionary trends and emerging hotspots in this field.AIM To study the subject trends and knowledge structures of gut microbiota related research fields from 2004 to 2018.METHODS The literature data on gut microbiota were identified and downloaded from the PubMed database.Through biclustering analysis,strategic diagrams,and social network analysis diagrams,the main trend and knowledge structure of research fields concerning gut microbiota were analyzed to obtain and compare the research hotspots in each period.RESULTS According to the strategic coordinates and social relationship network map,Clostridium Infections/microbiology,Clostridium Infections/therapy,RNA,Ribosomal,16S/genetics,Microbiota/genetics,Microbiota/immunology,Dysbiosis/immunology,Infla-mmation/immunology,Fecal Microbiota Transplantation/methods,Fecal Microbiota Transplantation can be used as an emerging research hotspot in the past 5 years(2014-2018).CONCLUSION Some subjects were not yet fully studied according to the strategic coordinates;and the emerging hotspots in the social network map can be considered as directions of future research.

The Impact of Urban Renewal on Public Service Facilities in Shenzhen during Inventory Development Stage:An Empirical Analysis of Futian District

As the material carrier of social service industry,public service facilities are important indicators to measure human settlement.With the continuous deepening of inventory development practice,urban renewal has gradually become one of the sources of supply of public service facilities.Focusing on the urban renewal activities of Futian District,Shenzhen from 2010 to 2016,the study conducts an empirical study on the pattern of public service facility networks before and after urban renewal through social network analysis.The fi ndings show that:①on the whole,the public service system in Futian District has been effectively improved during urban renewal development;②the impact of urban renewal on public service system is characterized by differentiation,among which industrial and commercial facilities have been signifi cantly improved,while educational and medical facilities have been hardly improved;③urban renewal has a clear policy orientation as an institutional means,and promoting economic restructuring and industrial reorganization is still the primary purpose of urban renewal during inventory development stage.

Optimization Processes of Tangible and Intangible Networks through the Laplace Problems for Regular Lattices with Multiple Obstacles along the Way

A systematic approach is proposed to the theme of safety,reliability and global quality of complex networks(material and immaterial)by means of special mathematical tools that allow an adequate geometric characterization and study of the operation,even in the presence of multiple obstacles along the path.To that end,applying the theory of graphs to the problem under study and using a special mathematical model based on stochastic geometry,in this article we consider some regular lattices in which it is possible to schematize the elements of the network,with the fundamental cell with six,eight or 2(n+2)obstacles,calculating the probability of Laplace.In this way it is possible to measure the“degree of impedance”exerted by the anomalies along the network by the obstacles examined.The method can be extended to other regular and/or irregular geometric figures,whose union together constitutes the examined network,allowing to optimize the functioning of the complex system considered.

Social Opinion Network Analytics in Community Based Customer Churn Prediction

Community based churn prediction,or the assignment of recognising the influence of a customer’s community in churn prediction has become an important concern for firms in many different industries.While churn prediction until recent times have focused only on transactional dataset(targeted approach),the untargeted approach through product advisement,digital marketing and expressions in customer’s opinion on the social media like Twitter,have not been fully harnessed.Although this data source has become an important influencing factor with lasting impact on churn management.Since Social Network Analysis(SNA)has become a blended approach for churn prediction and management in modern era,customers residing online predominantly and collectively decide and determines the momentum of churn prediction,retention and decision support.In existing SNA approaches,customers are classified as churner or non-churner(1 or 0).Oftentimes,the customer’s opinion is also neglected and the network structure of community members are not exploited.Consequently,the pattern and influential abilities of customers’opinion on relative members of the community are not analysed.Thus,the research developed a Churn Service Information Graph(CSIG)to define a quadruple churn category(churner,potential churner,inertia customer,premium customer)for non-opinionated customers via the power of relative affinity around opinionated customers on a direct node to node SNA.The essence is to use data mining technique to investigate the patterns of opinion between people in a network or group.Consequently,every member of the online social network community is dynamically classified into a churn category for an improved targeted customer acquisition,retention and/or decision supports in churn management.