Properties Of Social Networks Research Articles

Chronic health conditions and adolescent friendship: perspectives from social network analysis.

The current study uses methods from social network analysis to examine the relationship between chronic health conditions (CHCs) and adolescent friendships. Particular attention is given to the processes of peer marginalization, peer withdrawal and homophily related to CHCs. Exponential random graph models were used to investigate the extent to which a CHC is associated with patterns in adolescent friendship connections, while controlling for important social network properties and covariates. The study uses cross-sectional data from six small US high schools (n = 461) within the National Longitudinal Study of Adolescent to Adult Health. Findings demonstrate no significant differences between adolescents with CHCs and adolescents without CHCs in the number of incoming friendship nominations (peer marginalization) or outgoing friendship nominations (peer withdrawal). In addition, similarity in CHCs (homophily) was not significantly related to friendship between two individuals. In sum, the presence of an adolescent CHC was not significantly associated with adolescent social network structure, including peer marginalization, peer withdrawal, and homophily related to CHCs, after controlling for alternative social network processes. Although previous literature suggests that adolescents with CHCs experience negative social consequences, the current findings demonstrate that the social network structure of adolescents with CHCs did not differ significantly from that of their peers without CHCs. Thus, findings from the current study suggest that CHCs are not related to objective reductions in social connections.

Network Effects and Microenterprises: An Empirical Analysis of Microenterprises in China

Social networks play a crucial role in entrepreneurial processes and outcomes. However, little is known about the impact of social networks on microenterprises’ business performance. Addressing this gap, I focus on two dimensions of social networks, namely network size and resources embedded in a social network. Theoretically, combining insights from strategy research highlighting strategic needs as a determinant for external resource acquisition and the resource-based view of the firm suggesting that acquiring complementary resources can be a source of competitive advantage, I theorize why micro-entrepreneurs need to acquire external resources from their social networks, and why and how acquiring external resources are beneficial for their business performance. Empirically, I investigate the social network-performance relationship in the microenterprises context based on a dataset of 200 stratified randomly sampled Chinese micro-entrepreneurs in Shanghai. While the results suggest that micro-entrepreneurs’ network size per se does not relate to their business performance, I show that micro-entrepreneurs who can access to more varieties of resources embedded in their social networks tend to report a higher profit in a typical month. Furthermore, I find that micro-entrepreneurs whose external resources are more equally distributed across network contacts are associated with better business performance.

Influence Maximization in Social Network Considering Memory Effect and Social Reinforcement Effect

Social networks have attracted a lot of attention as novel information or advertisement diffusion media for viral marketing. Influence maximization describes the problem of finding a small subset of seed nodes in a social network that could maximize the spread of influence. A lot of algorithms have been proposed to solve this problem. Recently, in order to achieve more realistic viral marketing scenarios, some constrained versions of influence maximization, which consider time constraints, budget constraints and so on, have been proposed. However, none of them considers the memory effect and the social reinforcement effect, which are ubiquitous properties of social networks. In this paper, we define a new constrained version of the influence maximization problem that captures the social reinforcement and memory effects. We first propose a novel propagation model to capture the dynamics of the memory and social reinforcement effects. Then, we modify two baseline algorithms and design a new algorithm to solve the problem under the model. Experiments show that our algorithm achieves the best performance with relatively low time complexity. We also demonstrate that the new version captures some important properties of viral marketing in social networks, such as such as social reinforcements, and could explain some phenomena that cannot be explained by existing influence maximization problem definitions.

An Analysis of Social Networks Based on Tera-Scale Telecommunication Datasets

With the popularization of mobile phone usage, telecommunication networks have turned into a socially binding medium. Considering the traces of human communication held inside these networks, telecommunication networks are now able to provide a proxy for human social networks. To study degree characteristics and structural properties in large-scale social networks, we gathered a tera-scale dataset of call detail records that contains $\approx 5 \times 10^7$≈5×107 nodes and $\approx 3.6 \times 10^{10}$≈3.6×1010 links for three GSM (mobile) networks, as well as $\approx 1.4 \times 10^7$≈1.4×107 nodes and $\approx 1.9 \times 10^9$≈1.9×109 links for one PSTN (fixed-line) network. In this paper, we first empirically evaluate some statistical models against the degree distribution of the country's call graph and determine that a Pareto log-normal distribution provides the best fit, despite claims in the literature that power-law distribution is the best model. We then question how network operator, size, density, and location affect degree distribution to understand the parameters governing it in social networks. Our empirical analysis indicates that changes in density, operator and location do not show a particular correlation with degree distribution; however, the average degree of social networks is proportional to the logarithm of network size. We also report on the structural properties of the communication network. These novel results are useful for managing and planning communication networks.

Social networks of educational purpose as a subject of protection in the preparation of specialists of information security

The paper studies application of social networks as a component of the educational information environment of an educational institution in the educational process. The aim of the research is to justify the need to train specialists in the field of information security to provide protection and safe use of social networks in the educational process. To ensure information security of social networks applied in educational processes, it is proposed to incorporate training programs for information security specialists to build their competencies of information security of social networks, including gaining advanced skills to combat new challenging cyber threats, as well as to employ information security specialists with a high level of expertise in cyber security of social networks, determine their work functions in the elaboration of organizational and technical measures to protect users of social networks. In the course of the research, publications on incorporating social networks into the educational process as a didactic tool were analyzed. In particular, on the basis of the conducted analysis of the cited publications and taking into account the authors’ own experience, the properties of social networks that are most promising for their use as a learning platform were determined. However, along with the above positive aspects and advantages, social networks can also be considered as indirect and even direct sources of cyber threats targeted at both the entire educational process and individual users’ accounts. Theoretical and empirical pedagogical methods were used to accomplish the objectives. This facilitated efficient systematization of the collected data for its successful study and identification of the most typical features of the social networks when applied in the educational process and extension of those with specific properties were identified. The paper also describes threats that may arise when using such networks in an educational organization, suggests measures to mitigate risks and discusses functions of information security specialists in ensuring security of social networks used in the educational process. Also, it was established that for the effective and safe use of social networks in the educational process, the organizer of this activity requires specific professional competence in the field of information security, which can be insufficient among the overwhelming majority of lecturers of both general and vocational education. Moreover, a conclusion was drawn about the need to train specialists in the information security of social networks, so they can gain the required professional competences and execute their specific occupations. Proposals were made on the possible development of these competences in the course of studying certain disciplines at the level of the secondary vocational education. Integration of the described approach into the educational process of training information security specialists in institutions of secondary vocational education, aimed at providing maintenance of social networks, will allow meeting the demand for such specialists and, thereby, increasing their competitiveness in the market of educational services.

Enhancing the use of transit in arid regions: Case of Abu Dhabi

Abu Dhabi aims to provide world-class facilities and service in the region, including a good network of public transportation. In addition to the current bus service, it plans to add light rail transit, commuter rail service, tram, and ferry service in the future. Understanding the perception of current transit users can help provide service recommendation that can enhance the transit system in the region. This study uses the transit user stated preference survey to examine the trip characteristics; travel behavior; perception of quality of service, social network, and personal attributes of transit users. The transit user characteristics substantiate the existence of social exclusion and the need for multi-housing in-fill development in the station area. Recommendations to enhance the built environment and transit services are discussed to improve accessibility and rider experience of transit users in Abu Dhabi.

Task Allocation Model Based on Worker Friend Relationship for Mobile Crowdsourcing.

With the rapid development of mobile devices, mobile crowdsourcing has become an important research focus. According to the task allocation, scholars have proposed many methods. However, few works discuss combining social networks and mobile crowdsourcing. To maximize the utilities of mobile crowdsourcing system, this paper proposes a task allocation model considering the attributes of social networks for mobile crowdsourcing system. Starting from the homogeneity of human beings, the relationship between friends in social networks is applied to mobile crowdsourcing system. A task allocation algorithm based on the friend relationships is proposed. The GeoHash coding mechanism is adopted in the process of calculating the strength of worker relationship, which effectively protects the location privacy of workers. Utilizing synthetic dataset and the real-world Yelp dataset, the performance of the proposed task allocation model was evaluated. Through comparison experiments, the effectiveness and applicability of the proposed allocation mechanism were verified.

Scientific Literature based Big Data Analysis for Technology Insight

The increasingly massive amount and open access of literature provide a data foundation for technology insight based on big data analysis. This paper proposed a new technology insight framework -Technology Dependency Graph (TDG). Firstly, an adversarial multitask learning and distantly-supervised learning were applied to extract the technology entity and dependency relation with limited labeled sample. Then, a TDG was constructed with the entities as vertices and the dependency relations as edges. A TDG contains rich and valuable semantic information which represents the support, contribution or relying on relationship between technologies. At the same time, the social network properties of TDG allow researchers to analyze and mine hot topics, key technologies, and technology architecture by using network theories, methods and tools. In the case study, the TDG of DSSC (dye-sensitized solar cell) was constructed. Furthermore, the technology dependency architecture for DSSC was constructed according to a spanning tree out of the TDG, which provides a global perspective for the research of DSSC.

Diffusion of cryptocurrencies: web traffic and social network attributes as indicators of cryptocurrency performance

This study investigates the popularity, global and local presence of the top cryptocurrency websites in terms of market capitalization, and geographic distribution of cryptocurrency information on the web by mapping the information network of cryptocurrency sites. The study also explores whether web traffic and social network metrics serve as valuable indicator of financial performance of cryptocurrencies. Both citation and social network analyses were employed. The results suggest that bitcoin.org, bitcoin.com and steemit.com are the most popular sites based on the number of hit counts. Information production and consumption about coins are concentrated in European and Asia–Pacific regions. Germany and technology startup companies are the most central sources for cryptocurrency-related information in the network. This study also found positive cross correlation outcomes between web traffic, social network attributes, and cryptocurrency performance indicators. The finding demonstrates that the number of TLDs and multiple social network centrality measures are useful indicators of cryptocurrency market capitalization, trading volume, and price.

Social Capital and Farmers’ Income in Maize (Zea mays) Production

Social capital is an investment to get new resources in society. Weak social capital will dim the spirit of mutual cooperation, exacerbate poverty, increase unemployment, crime, and hinder any effort in improving the welfare of the population including farmers. Utilizing the potential of social capital had been able to contribute to improving income of farmers in the rural area. So that, it is important to analyze the significance of influence in social capital and farmers’ income of maize production in Jeneponto, South Sulawesi. Data were collected by structured interview through probability proportional to 120 of maize farmers as samples which were analyzed by descriptive analysis. Final resulted showed that Social capital through the dimensions of trust, social network and norms had a positive effect on farmers' income, but the effect was not significant. The dimensions of social capital owned by new farmers formed a positive attitude that supported and had not directly influenced the increase in farmers' income, especially the increase in production as the highest dimension of income generation.

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.

Turning into a “Godparent”: How adult volunteers negotiate their personal life to become a mentor for “Unaccompanied Refugee Minors”

This article looks into how mentors deal with their biographies and social embeddedness to make sense of their engagement in mentoring before they are matched. It draws on a qualitative investigation carried out during a pilot youth mentoring program for “unaccompanied refugee minors” in Austria. This article reveals how already trained, local adult volunteers actively relate to “family,” “migration” and “previous activities” in their meaning-making. It shows how they negotiate their personal life and existing relationships in the process of turning into a future “godparent.” The discussion of findings against the state of the art leads the way to two heuristic claims: firstly, the study provides grounded arguments for an extension of the conventional mentoring concept on the side of the mentor. Secondly, for a more relational and processual approach towards the mentors’ side, both biographical and social network dimensions need to be integrated in methods and designs of youth mentoring research.

2-Hopper: Accurately Estimate Individual and Social Attributes of Social Networks With Fewer Repeats via Random Walk

Random-walk based sampling is widely used to characterize large graphs by producing samples in the form of nodes. However, existing random-walk based sampling methods only focus on the estimation accuracy of structural properties but suffer from repetitive samples which have adverse effects on obtaining accurate information about the structures over social networks represented by large graphs. Furthermore, these existing methods mainly characterize individual attributes while ignoring the social attributes of the nodes. In this paper, a new random-walk based method, called 2-hop neighbors based random walk or 2-Hopper, is proposed to obtain accurate estimations of both basic and social attributes with fewer repetitive samples. Specifically, 2-Hopper is able to greatly reduce redundant paths among nodes during the sampling process and thus produces few repeats. Based on 2-Hopper’s sampling process, a re-weighted estimator is proposed to accurately obtain both the individual and social properties while the latter is obtained by a newly proposed algorithm. Experimental results driven by real-world datasets show that on average 2-Hopper can reduce 4.5 times repetitive samples of the state-of-the-art random-walk based methods and obtain more accurate information about the individual and social attributes while 2-Hopper is able to estimate the structural properties of these attributes accurately over large graphs.

A Study on the Use of Social Networks by Turkish and German University Students in the Globalization Process

Today's rapidly evolving technology is expanding the use of innovative communication technologies and their usage areas. to traditional communication technologies today; smartphones, laptop computers, handheld computers, and tablets are also added. Wireless communication technology removes time and space limits, allowing people to communicate both voiced and visual whenever and wherever they wish. Every day, millions of people communicate with each other through social networking networks and share their experience day by day with other network users. The social networks that people often use are also affecting interpersonal relationships. The purpose of this research is to determine the aims of Turkish and German university students to use social networks and how effective social influence is in interpersonal communication. A total of 338 students, 236 Turkish students studying at Karabük University and 102 German students studying at Kassel University in Germany, participated in the research in the academic year of 2016-2017. As a data collection tool, a 10-item questionnaire developed by Özdayı (2010) and a 13-item, “social impact scale” were used. 4 items of the questionnaire used in the study were arranged in the form of "yes-no" and the other items were arranged by the participants to point to the box opposite to the statement they found appropriate. Each participant can mark a few of the options suitable for him / her. Secondly, the "social impact scale" is 5-Likert type. In the face of each article (5) from its fully appropriate expression, (1) Not suitable at all, a gradation to the statement was made. Percentages, mean and t test for binary comparison were used as statistical analysis in the study. According to research findings; all students have smartphones and they use whatsapp, facebook and youtube most from social networks. By students, social media is used to look at mails, homework, study, follow current events, read news, communicate with friends, make new friends, get informed about activities, share videos and photos and have fun. Also by students travel, shopping, technology and cinema blogs are the most preferred. In the survey, in the social dimension of social networks; there was no significant difference between the groups regarding "communication, self-expression, staying out of the group, becoming popular, joining groups, getting social environment, getting status in social environment and sharing". On the other hand, social networking has become an important means of communication and interaction among people today. For this reason, academicians should encourage students who are interested in new technology and communication applications to support the achievement of up-to-date information within the context of lifelong learning, and to conduct research for their own development in the teaching-learning process.

An interpretable approach for social network formation among heterogeneous agents

Understanding the mechanisms of network formation is central in social network analysis. Network formation has been studied in many research fields with their different focuses; for example, network embedding algorithms in machine learning literature consider broad heterogeneity among agents while the social sciences emphasize the interpretability of link formation mechanisms. Here we propose a social network formation model that integrates methods in multiple disciplines and retain both heterogeneity and interpretability. We represent each agent by an “endowment vector” that encapsulates their features and use game-theoretical methods to model the utility of link formation. After applying machine learning methods, we further analyze our model by examining micro- and macro- level properties of social networks as most agent-based models do. Our work contributes to the literature on network formation by combining the methods in game theory, agent-based modeling, machine learning, and computational sociology.

Teaching-focused social networks among college faculty: exploring conditions for the development of social capital

Scholars have long recognized that teachers’ social interactions play an important role in their learning and professional development. Still, while a growing body of research shows that teaching-focused social ties can give precollege educators access to valuable information, knowledge, and advice—or “social capital”—that improves professional practice and student learning, empirical, mixed methods studies on the phenomenon in the higher education sector are rare, and few investigate what conditions are necessary for these social ties to develop among college instructors. Focusing on college faculty in 17 associate- and baccalaureate-level institutions in one U.S. city, this study uses survey and interview data to explore the connections between structural and positional educator characteristics and the “social networks,” or compilations of social ties, in which faculty reported discussing teaching. Regression analyses of survey responses (n = 244) indicate that fewer years of teaching experience, the time faculty take preparing to teach, discipline, and institution type are correlated with social network dimensions linked to improved professional practice. An inductive analysis of interview data from a subset of faculty (n = 22) supplements survey findings with descriptions of how teaching experience, organizational support, and other factors constrain and reinforce the development of teaching-focused social ties. Results confirm and extend prior research indicating that the development of teaching-focused social networks and the accrual of ties linked to social capital demand faculty and organizational investment. Findings also suggest that leaders hoping to foster beneficial ties should tailor instructional initiatives to more closely align with faculty experience and time commitments.

The web of host–guest connections on Airbnb: a network perspective

PurposeThe purpose of this paper is to explore Airbnb’s inherent network structure emerging from transactions between hosts and guests and provide comprehensive background information on the underlying data basis.Design/methodology/approachThe analysis is based on actual Airbnb data from 16 major US cities (Asheville, Austin, Boston, Chicago, Denver, Los Angeles, Nashville, New Orleans, New York City, Oakland, Portland, San Diego, San Francisco, Santa Cruz, Seattle and Washington DC), available at InsideAirbnb.com, comprising a total of 135 thousand listings and 2.7 million transactions. The data are transformed into a graph and analyzed from a network perspective.FindingsThe web of host–guest connections on Airbnb represents a omniferous graph, that is, connecting virtually all users via relatively short distances. Hosts and guests differ markedly with regard to degree distribution. Overall, 98 per cent of all transactions represent first-time encounters.Research limitations/implicationsThis paper provides first insights into the very fabric of host–guest interactions on Airbnb from a macroscopic perspective. The platform’s network topology may be leveraged as a resource for trust-building between users. Moreover, platform operators may use network analyses to gain deeper insights into their user base. These may in turn be used to identify determinants of side-switching, deter users from platform circumvention or for churn prevention.Originality/valuePlatform ecosystems continue to expand and gain increasing economic, social and societal importance. For C2C platforms with two compartmentalized and decentral market sides (i.e. many individual providers and many individual consumers), the emerging transactional network structure has, thus, far experience almost no research attention. This analysis of Airbnb’s web of host–guest connections reveals a topology some archetypical social network properties (e.g. short distances). This structure and the knowledge about users’ positions therein yields viable cues for trust-building as well as a valuable resource for (platform) business analytics.

The relevance of social network tools using in educational process

The article considers the characteristic properties of social networks. The existing of social networks and their possibilities (main advantages and problems) in terms of using in the educational process are analyzed. The purpose of article is to investigate the teachers and students possibility and readiness to using of social networks in educational process to ensure more effective collaboration in the interactive mode. The possibilities and readiness of students and teachers of Odessa National Economic University (ONEU) to use social networks in the educational process are analyzed. The SWOT-analysis on social networks attraction in the ONEU educational process is given. The advantages and limitations of IT-technologies in the educational process for students and teacher are considered. The tools of social networks application and examples of their using in educational process are offered. An important factor, which encumbering of social networking tools implementation in educational process are the unavailability of this process to attract modern teaching aids, because in the study, the authors concluded that there is a lack of scientifically grounded guidelines, time-consuming norms and teacher’s remuneration. These problems can be solved by conditions creating for advanced of teachers training in the information and communication technologies sphere, namely: the material and moral teachers incentives, who are actively using a new technologies and developing of effective methods for social networks applying in the university educational space. Social networks are not main means of learning, but their opportunities to educational problems solving is underestimated by the professional community today.

Editorial board interlocking in knowledge management and intellectual capital research field

Due to the importance of academic journals, it is critical to understand how they can create influence on research fields. An alternative way to understand how scholarly journals create influence in academic fields is to analyze the editorial board interlockings (EBI) and how they may generate networks within academic fields. EBI is the term used to describe the general circumstance where the same scholar is a member of multiple editorial boards, which creates a social network dimension within and between academic journals. The aim of this paper is to examine EBI phenomenon within knowledge management and intellectual capital fields (KM–IC). Assuming that EBI creates a social structure within scholarly journals, this paper investigates how KM–IC journals are connected through EBI, which journals are the most influential within KM–IC field and identify if KM–IC scholarly journal network breaks into subgroups. Social network analysis was the method applied using data from KM–IC ranking. Results identified the scholarly journal network, the high influence journals, and the cohesive subgroups within KM–IC field.

Social network properties predict chronic aggression in commercial pig systems.

Post-mixing aggression in pigs is a harmful and costly behaviour which negatively impacts both animal welfare and farm efficiency. There is vast unexplained variation in the amount of acute and chronic aggression that dyadic behaviours do not fully explain. This study hypothesised that certain pen-level network properties may improve prediction of lesion outcomes due to the incorporation of indirect social interactions that are not captured by dyadic traits. Utilising current SNA theory, we investigate whether pen-level network properties affect the number of aggression-related injuries at 24 hours and 3 weeks post-mixing (24hr-PM and 3wk-PM). Furthermore we compare the predictive value of network properties to conventional dyadic traits. A total of 78 pens were video recorded for 24hr post-mixing. Each aggressive interaction that occurred during this time period was used to construct the pen-level networks. The relationships between network properties at 24hr and the pen level injuries at 24hr-PM and 3wk-PM were analysed using mixed models and verified using permutation tests. The results revealed that network properties at 24hr could predict long term aggression (3wk-PM) better than dyadic traits. Specifically, large clique formation in the first 24hr-PM predicted fewer injuries at 3wk-PM and high betweenness centralisation at 24hr-PM predicted increased rates of injury at 3wk-PM. This study demonstrates that network properties present during the first 24hr-PM have predictive value for chronic aggression, and have potential to allow identification and intervention for at risk groups.