Citation and bibliographic coupling between authors in the field of social network analysis

Purpose:We analyzed the structure of a community of authors working in the field of social network analysis(SNA)based on citation indicators:direct citation and bibliographic coupling metrics.We observed patterns at the micro,meso,and macro levels of analysis.Design/methodology/approach:We used bibliometric network analysis,including the“temporal quantities”approach proposed to study temporal networks.Using a two-mode network linking publications with authors and a one-mode network of citations between the works,we constructed and analyzed the networks of citation and bibliographic coupling among authors.We used an iterated saturation data collection approach.Findings:At the macro-level,we observed the global structural features of citations between authors,showing that 80%of authors have not more than 15 citations from other works.At the meso-level,we extracted the groups of authors citing each other and similar to each other according to their citation patterns.We have seen a division of authors in SNA into groups of social scientists and physicists,as well as into other groups of authors from different disciplines.We found some examples of brokerage between different groups that maintained the common identity of the field.At the micro-level,we extracted authors with extremely high values of received citations,who can be considered as the most prominent authors in the field.We examined the temporal properties of the most popular authors.Research limitations:The main challenge in this approach is the resolution of the author’s name(synonyms and homonyms).We faced the author disambiguation,or“multiple personalities”(Harzing,2015)problem.To remain consistent and comparable with our previously published articles,we used the same SNA data collected up to 2018.The analysis and conclusions on the activity,productivity,and visibility of the authors are relative only to the field of SNA.Practical implications:The proposed approach can be utilized for similar objectives and identifying key structures and characteristics in other disciplines.This may potentially inspire the application of network approaches in other research areas,creating more authors collaborating in the field of SNA.Originality/value:We identified and applied an innovative approach and methods to study the structure of scientific communities,which allowed us to get the findings going beyond those obtained with other methods.We used a new approach to temporal network analysis,which is an important addition to the analysis as it provides detailed information on different measures for the authors and pairs of authors over time.

Network analysis of the relationships between depressive symptoms and social participation activities among Chinese older adults and its implications for nursing

Objective:Network analysis was used to explore the complex inter-relationships between social participation activities and depressive symptoms among the Chinese older population,and the differences in network structures among different genders,age groups,and urban-rural residency would be compared.Methods:Based on the 2018 wave of the Chinese Longitudinal Healthy Longevity Survey(CLHLS),12,043 people aged 65 to 105 were included.The 10-item Center for Epidemiologic Studies Depression(CESD)Scale was used to assess depressive symptoms and 10 types of social participation activities were collected,including housework,tai-chi,square dancing,visiting and interacting with friends,garden work,reading newspapers or books,raising domestic animals,playing cards or mahjong,watching TV or listening to radio,and organized social activities.R 4.2.1 software was used to estimate the network model and calculate strength and bridge strength.Results:21.60%(2,601/12,043)of the participants had depressive symptoms.The total social participation score was negatively associated with depressive symptoms after adjusting for sociodemographic factors.The network of social participation and depressive symptoms showed that“D9(Inability to get going)”and“S9(Watching TV and/or listening to the radio)”had the highest strength within depressive symptoms and social participation communities,respectively,and“S1(Housework)”,“S9(Watching TV and/or listening to the radio)”,and“D5(Hopelessness)”were the most prominent bridging nodes between the two communities.Most edges linking the two communities were negative.“S5(Graden work)-D5(Hopelessness)”and“S6(Reading newspapers/books)-D4(Everything was an effort)”were the top 2 strongest negative edges.Older females had significantly denser network structures than older males.Compared to older people aged 65e80,the age group 81e105 showed higher network global strength.Conclusions:This study provides novel insights into the complex relationships between social participation and depressive symptoms.Except for doing housework,other social participation activities were found to be protective for depression levels.Different nursing strategies should be taken to prevent and alleviate depressive symptoms for different genders and older people of different ages.

The Research on E-mail Users' Behavior of Participating in Subjects Based on Social Network Analysis

The e-mail network is a type of social network. This study analyzes user behavior in e-mail subject participation in organizations by using social network analysis. First, the Enron dataset and the position-related information of an employee are introduced, and methods for deletion of false data are presented. Next, the three-layer model(User, Subject, Keyword) is proposed for analysis of user behavior. Then, the proposed keyword selection algorithm based on a greedy approach, and the influence and propagation of an e-mail subject are defined. Finally, the e-mail user behavior is analyzed for the Enron organization. This study has considerable significance in subject recommendation and character recognition.

Social network analysis and modeling of cellphone-based syndromic surveillance data for ebola in Sierra Leone

Objective:To explore and visualize the connectivity of suspected Ebola cases and surveillance callers who used cellphone technology in Moyamba District in Sierra Leone for Ebola surveillance,and to examine the demographic differences and characteristics of Ebola surveillance callers who make more calls as well as those callers who are more likely to make at least one positive Ebola call.Methods:Surveillance data for 393 suspected Ebola cases(192 males,201 females) were collected from October 23,2014 to June 28,2015 using cellphone technology.UCINET and Net Draw software were used to explore and visualize the social connectivity between callers and suspected Ebola cases.Poisson and logistic regression analyses were used to do multivariable analysis.Results:The entire social network was comprised of 393 ties and 745 nodes.Women(AOR=0.33,95% CI [0.14,0.81]) were associated with decreased odds of making at least one positive Ebola surveillance call compared to men.Women(IR= 0.63,95% CI [0.49,0.82]) were also associated with making fewer Ebola surveillance calls compared to men.Conclusion:Social network visualization can analyze syndromic surveillance data for Ebola collected by cellphone technology with unique insights.

A Parallel Approach for Sentiment Analysis on Social Networks Using Spark

The public is increasingly using social media platforms such as Twitter and Facebook to express their views on a variety of topics.As a result,social media has emerged as the most effective and largest open source for obtaining public opinion.Single node computational methods are inefficient for sentiment analysis on such large datasets.Supercomputers or parallel or distributed proces-sing are two options for dealing with such large amounts of data.Most parallel programming frameworks,such as MPI(Message Processing Interface),are dif-ficult to use and scale in environments where supercomputers are expensive.Using the Apache Spark Parallel Model,this proposed work presents a scalable system for sentiment analysis on Twitter.A Spark-based Naive Bayes training technique is suggested for this purpose;unlike prior research,this algorithm does not need any disk access.Millions of tweets have been classified using the trained model.Experiments with various-sized clusters reveal that the suggested strategy is extremely scalable and cost-effective for larger data sets.It is nearly 12 times quicker than the Map Reduce-based model and nearly 21 times faster than the Naive Bayes Classifier in Apache Mahout.To evaluate the framework’s scalabil-ity,we gathered a large training corpus from Twitter.The accuracy of the classi-fier trained with this new dataset was more than 80%.

Mathematical Modeling in Social Network Analysis： Using TOPSIS to Find Node Influences in a Social Network

In a social network analysis the output provided includes many measures and metrics. For each of these measures and metric, the output provides the ability to obtain a rank ordering of the nodes in terms of these measures. We might use this information in decision making concerning disrupting or deceiving a given network. All is fine when all the measures indicate the same node as the key or influential node. What happens when the measures indicate different key nodes？ Our goal in this paper is to explore two methodologies to identify the key players or nodes in a given network. We apply TOPSIS to analyze these outputs to find the most influential nodes as a function of the decision makers＇ inputs as a process to consider both subjective and objectives inputs through pairwise comparison matrices. We illustrate our results using two common networks from the literature： the Kite network and the Information flow network from Knoke and Wood. We discuss some basic sensitivity analysis can may be applied to the methods. We find the use of TOPSIS as a flexible method to weight the criterion based upon the decision makers＇ inputs or the topology of the network.

Dynamic Social Network Analysis with Heterogeneous Sensors in Ambient Environment

This paper presents our vision of large-scale, dynamic social network analysis in real environments, which we expect to be enabled by the introduction of large-scale heterogeneous sensors in the ambient environment. We address challenges in realizing large-scale dynamic social network analysis in real environments, and discuss several promising applications. Moreover, we present our design and implementation of a prototype system for quasi-realtime social network construction. We finally present preliminary experimental results of dynamic social network analysis for six-person social gatherings in a real environment, and discuss the feasibility of dynamic social network analysis and its effectiveness.

How Do the Evolution and Innovation of Social Network Analysis Matter to Computer Science and Communications?

This paper provides a comprehensive overview of evolution and innovation in social network analysis to the paradigm of social networking. It explains how the development of sociological theory and the structural properties of social groups matter to computer science and communications. Authors such as Moreno, John Barnes and Harrison C. White provide evidence of a growing body of literature addressing the networking of people, organizations and communities to explain the structure of society. This perspective has passed from sociology to other fields, changing understandings of social phenomena. Social networks remain a potent concept for analyzing computer science and communications. This paper shows how and why this has occurred and examines substantive areas in which social network analysis has been applied—mainly how the advantages of graphic visualization and computer software packages have influenced SNA in different audiences and publics leading to the unfolding of social networking to different audiences and publics.

The Influence Factors of Collective Intelligence Emergence in Knowledge Communities Based on Social Network Analysis

The advent of the age of Information shifts the environment we live in from off-line to on-line. The prospect of Collective Intelligence (CI) is promising. Based on this background, the aim of this paper is to discover the emergence mechanism and influence factors of CI in knowledge communities using the method of quantitative and qualitative analysis. On the basis of the previous research work, our model theorizes that the two dimensions of social network (i.e., interactive network structure and participant’s characteristics) affect two references of effectiveness (i.e. group knowledge production and participation of group decision). And this hypothetical model is validated with simulation data from “Zhihu” community. Our model has been useful since it offers some inspirations and directions to promote the level of CI in knowledge communities.

Social network search based on semantic analysis and learning

Because of everyone＇s involvement in social networks, social networks are full of massive multimedia data, and events are got released and disseminated through social networks in the form of multi-modal and multi-attribute heterogeneous data. There have been numerous researches on social network search. Considering the spatio-temporal feature of messages and social relationships among users, we summarized an overall social network search framework from the perspective of semantics based on existing researches. For social network search, the acquisition and representation of spatio-temporal data is the basis, the semantic analysis and modeling of social network cross-media big data is an important component, deep semantic learning of social networks is the key research field, and the indexing and ranking mechanism is the indispensable part. This paper reviews the current studies in these fields, and then main challenges of social network search are given. Finally, we give an outlook to the prospect and further work of social network search.

Research on the Association of Mobile Social Network Users Privacy Information Based on Big Data Analysis

The issue of privacy protection for mobile social networks is a frontier topic in the field of social network applications.The existing researches on user privacy protection in mobile social network mainly focus on privacy preserving data publishing and access control.There is little research on the association of user privacy information,so it is not easy to design personalized privacy protection strategy,but also increase the complexity of user privacy settings.Therefore,this paper concentrates on the association of user privacy information taking big data analysis tools,so as to provide data support for personalized privacy protection strategy design.

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.

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.

Empirical Study on Growth of Evil Forces in Land Requisition and Relocation in City G of Hubei Province Based on Social Network Analysis

Using social network analysis method,this paper made an empirical study on growth of evil forces in land requisition and relocation in City G of Hubei Province. It obtained following results:(i) lawless developers and inefficient public security organs form interested parties of evil forces. Besides,the inward closeness centrality of evil forces is high,manifesting that evil forces independently possess decentralized power of network and have unscrupulous behavior in land requisition and relocation to a certain extent.(ii) Activities of evil forces have complicated spatial correlation and their geographical distribution is uneven,taking on irregular characteristics. In the field of land requisition and relocation,some evil forces are expandi Xng and spreading,while other forces are relatively weak. In conclusion,growth of evil forces comes from premeditation and collaboration of lawless developers,lack of functions and weak attack of public security organs; growth of evil forces has periodic changes,when in power,they will take opportunity to expand,while losing power,they will hide or even disappear.

Using Social Networks to Support Software Ecosystems Comprehension and Evolution

The software industry has evolved to a multiple-product development created on a platform and based on a common architecture integrated to other systems. This integration happens through components and third-party developers networks in Software Ecosystems (SECOs). Since systems and software development processes present challenges beyond the technical side, SECOs have emerged as an approach to improve the Software Engineering (SE) mindset in the industry. This fact changes the software industry as it requires the management of an integrated social-based environment to support a transition from an intra-organizational to an open business model approach towards a SECO approach. In this context, social networks can be important to coordinate a collaborative and distributed environment to develop SECOs platforms. This paper analyses the impact of social networks in SECOs through an integrated framework of the SECO and social network challenges. So, a proposal for a sociotechnical-based architecture to support the SECOs lifecycle is discussed.

Social Networks, Politics and Public Views: An Analysis of the Term “Macedonia” in Twitter

In this paper we deal with Twitter and the presence of the keyword “Macedonia” in tweets over a period of time. We searched for the same term in three different languages, i.e. “Μακεδονíα”, “Macedonia” and “Македонска - Македониjа”, since we are primarily interested in views from Greece and FYROM without excluding views from other regions. We use methods from Social Network Analysis (SNA) in order to create networks of users, calculate some main network metrics, measure user importance and investigate the presence of possible fragmentations—communities among them. We furthermore proceed to a form of content analysis, using pairs of words within tweets, in order to obtain main ideas, trends and public views that circulated over the network.

Novel Epistemic and Predictive Heuristic for Semantic and Dynamic Social Networks Analysis

Using Kripke semantics, we have identified and reduced an epistemic incompleteness in the metaphor commonly employed in Social Networks Analysis (SNA), which basically compares information flows with current flows in advanced centrality measures. Our theoretical approach defines a new paradigm for the semantic and dynamic analysis of social networks including shared content. Based on our theoretical findings, we define a semantic and predictive model of dynamic SNA for Enterprises Social Networks (ESN), and experiment it on a real dataset.

Spatial Social Network Analysis: Program Pengembangan Usaha Agribisnis Perdesaan (PUAP) or an Exertion Development Program in Supporting the Region Revitalization Development

As the exertion development (PUAP) develop in Rejang Lebong Regency in Bengkulu province which had been started since 2008 by giving a kind of fund aid called Bantuan Langsung Masyarakat (BLM PUAP), to Gapoktan (Gabungan Kelompok Tani) or an organization of farmers such by giving some money as the capital used for 1) food plant cultiva-tion, horticulture, animal husbandry, plantation, and2) non-cultivation exertion such as;home industry of agriculture, small marketing scale and another agriculture exertion. By the approach which had been done is hoped to get the important result, on the other way the role of analysis factor (Social Network Analysis) from all undertaking stakeholders programs PUAP to be relevance steps aimed to understand the relation or connectivity from all factors in the network and also the role of factor inside the network itself be the relevance steps in arranging the strategy and work intention from the realization stage of PUAP program for the continuity of the realization program in the future (finding the good strategy) which aimed to develop the program of PUAP in Rejang Lebong Regency in Bengkulu Province, and to be a reference from doing the program as a national program in Indonesia.

Sentiment Analysis on the Social Networks Using Stream Algorithms

The rising popularity of online social networks (OSNs), such as Twitter, Facebook, MySpace, and LinkedIn, in recent years has sparked great interest in sentiment analysis on their data. While many methods exist for identifying sentiment in OSNs such as communication pattern mining and classification based on emoticon and parts of speech, the majority of them utilize a suboptimal batch mode learning approach when analyzing a large amount of real time data. As an alternative we present a stream algorithm using Modified Balanced Winnow for sentiment analysis on OSNs. Tested on three real-world network datasets, the performance of our sentiment predictions is close to that of batch learning with the ability to detect important features dynamically for sentiment analysis in data streams. These top features reveal key words important to the analysis of sentiment.