Evaluation Of Social Networks Research Articles

More Talk, More Support? The Effects of Social Network Interaction and Social Network Evaluation on Social Support via Social Media.

Do more friends and more frequent interactions on social media result in more social support? The impact of variables such as interaction size and frequency on the social support individuals receive via social platforms has been studied from the perspective of social networks, and some studies have focused on the role of interaction topics from the perspective of private engagement. Little research has been done on the impact of affect control embedded in social networks. The emotion-first nature of social media and the rise of affect control theory mean that this perspective deserves attention. This study examined 634 WeChat users by means of an online survey on variables related to social media use, such as social network size, interaction frequency, users' evaluation of network members, and personal topic involvement, and then tested the influencing mode of these variables on people's perceived social support through chain mediation analysis using Model 80 of the process in spss. (1) Interaction size influences perceived social support by affecting personal topic involvement; (2) Interaction frequency does not directly mediate the relationship between interaction size and perceived social support, but mediates the relationship in a chain by affecting personal topic involvement; and (3) Social network evaluation not only directly mediates the relationship between interaction size and perceived social support, but also further mediates the relationship by influencing personal topic involvement. The study confirmed the role of affective control in people's perceived social support through social network interactions. Those people talk about personal matters with individuals who have greater evaluation, potency, and activity in social networks are more likely to get perceived social support. The study draws our attention to the role of affection control in interpersonal relationships in the era of social media mediating people's daily interactions.

Abstract P214: Social Network Analysis of a Community Aphasia Group Pre- And Post-Covid: Implications for Future Planning

Introduction: Among the many negative consequences of aphasia is an altered social network. Social network analysis supports an objective, quantitative evaluation of social networks among individuals with aphasia along with potential impacts of social programming and interventions on an individual’s social network. Social network analysis may also support better understanding of the impact of Covid on individuals with aphasia. Aims: This pilot evaluation utilized social network analysis via R to evaluate the social network characteristics of a community-based aphasia network across a 12-month pre-Covid period. Social network aphasia group data for a standard duration of time pre- and post-Covid were also compared to identify potential social implications of Covid in a population already at higher risk for reduced social interactions. This presentation will also provide fundamental concepts relevant to social network analysis for those interested in pursuing such analysis in further depth. Methods: Twelve months of pre-Covid aphasia group program attendance data were examined using the visNetwork R package. An additional six months of Covid-era time frame data were also analyzed.The primary relationship function of “ a attended b” (where a = individual participant and b = event/setting) was used in the analysis. Multiple social network characteristics were analyzed and displayed including node, edgeness, directionality, weight, and centrality indices across individuals with aphasia, care partners and community members and settings. Results and Conclusions: Network analysis reveals a directed network graph with primarily unidirectional relationships. There is an emergence of several aphasia group participant behavior types, both pre- and post-Covid, relevant for future planning including: communities of individuals who have similar behaviors in terms of type of event attendance; key individuals who are "heavy users" of various services in terms of frequency and breadth of event attendance; and peripheral users who use only one service. Post-Covid social network implications are discussed including supports to mitigate negative impacts of Covid on social network composition.

Territorial intelligence in rural areas: The digitization of non-profit associations through social media

Rural communities are facing the challenge of technological development to address territorial management, by overcoming the digital divide, by introducing digitization in these resilient areas and connecting them. This article thus analyses the relationship that exists between territorial intelligence and development in rural areas through the use of social networks in non-profit associations. The aims are to establish the monitoring and evaluation of social networks in the Local Action Groups (LAGs) of Andalusia and to establish whether the demographic vacuum conditions interactivity.Fourteen indicators have been developed that are linked to social networks, using the LAGs’ websites—Facebook, Twitter and YouTube—as sources. The purpose of all this is to examine the volume of information and the degree of visibility that exists on each of the three platforms. Three indices of interactivity are obtained which are finally included in a cluster analysis.The results show that these associations use social networks more as a means of unidirectional communication with little interaction with society at large. Facebook continues to be the most widely used tool for spreading information in the rural environment. Although the majority of LAGs make an effort to communicate with each other, they should create more synergies. Rural depopulation need not hinder interactivity; a change of mentality is needed, along with the creation of new cultural paradigms for the delivery and use of public services.

An evaluation of social networks within federally funded research projects

United States federal agencies fund research to promote discovery and innovation. Most agencies require collaboration because teams promote productivity to a greater degree than singular researchers. However, the functionality and productivity of collaboration is poorly understood. The purpose of this study was to evaluate the collaborative structure of a federally funded entomology research team to determine the characteristics of the network structure and its impact on research collaboration using social network analysis (SNA) methodology. An online survey and interviews were used to collect data. The theories of social network, strong and weak ties, and scientific collaboration were employed to determine the degree of collaboration among team members. We found a low density pattern of collaboration that was associated with: (a) a centralized pattern, (b) the presence of sub-teams functioning like sub-networks, and (c) the presence of less interactive members. Our results confirm that the SNA approach was useful for evaluating network collaboration with innovative indicators to assess the dynamics of scientific collaboration. The study was limited by non-response. Future research should focus on collecting SNA data longitudinally of the whole network to determine how networking structure and benefits evolves over time, and how strong and weak ties impact scientific discovery.

Combining social network analysis and agent-based modelling to explore dynamics of human interaction: A review

Agent-based modelling (ABM) and social network analysis (SNA) are both valuable tools for exploring the impact of human interactions on a broad range of social and ecological patterns. Integrating these approaches offers unique opportunities to gain insights into human behaviour that neither the evaluation of social networks nor agent-based models alone can provide. There are many intriguing examples that demonstrate this potential, for instance in epidemiology, marketing or social dynamics. Based on an extensive literature review, we provide an overview on coupling ABM with SNA and evaluating the integrated approach. Building on this, we identify current shortcomings in the combination of the two methods. The greatest room for improvement is found with regard to (i) the consideration of the concept of social integration through networks, (ii) an increased use of the co-evolutionary character of social networks and embedded agents, and (iii) a systematic and quantitative model analysis focusing on the causal relationship between the agents and the network. Furthermore, we highlight the importance of a comprehensive and clearly structured model conceptualization and documentation. We synthesize our findings in guidelines that contain the main aspects to consider when integrating social networks into agent-based models.

An Evaluation of Social Networks within a Federally Funded Research Project

Social networks play an important role in creating positive outcomes for members of social structures, for example, highly networked individuals experience increased performance, productivity, and scholarly outputs in academia. Social network analysis (SNA) provides researchers with an effective method to evaluate the functionality of teams and, consequently, goal achievement through documenting self-reported connections among team members. Therefore, the purpose of the research reported here was to present an evaluation approach for capturing the development and structure of social networks among members of a large USDA, NIFA funded research and Extension project housed at an R1 land-grant research institution. We concluded that: (a) team members were informally and professionally acquainted prior to the project, (b) mentor – mentee relationships established a framework for developing the next generation of scientists, (c) subgroups existed within the network as information and data sharing was centralized around a few participants, and (d) there was a communication gap between university graduate students and industry representatives who served as advisory board members for the project Recommendations for improving team functionality include: (a) improving communication among team members, and (b) implementing a mentoring program to pair university students with industry representatives to improve the relevance of academic research products among growers.

Deep Reinforcement Learning Based on Link Prediction Method in Social Network Analysis

Improving the performance of link prediction is a significant role in the evaluation of social network. Link prediction is known as one of the primary purposes for recommended systems, bio information, and web. Most machine learning methods that depend on SNA model’s metrics use supervised learning to develop link prediction models. Supervised learning actually needed huge amount of data set to train the model of link prediction to obtain an optimal level of performance. In few years, Deep Reinforcement Learning (DRL) has achieved excellent success in various domain such as SNA. In this paper, we present the use of deep reinforcement learning (DRL) to improve the performance and accuracy of the model for the applied dataset. The experiment shows that the dataset created by the DRL model through self-play or auto-simulation can be utilized to improve the link prediction model. We have used three different datasets: JUNANES, MAMBO, JAKE. Experimental results show that the DRL proposed method provide accuracy of 85% for JUNANES, 87% for MAMABO, and 78% for JAKE dataset which outperforms the GBM next highest accuracy of 75% for JUNANES, 79% for MAMBO and 71% for JAKE dataset respectively trained with 2500 iteration and also in terms of AUC measures as well. The DRL model shows the better efficiency than a traditional machine learning strategy, such as, Random Forest and the gradient boosting machine (GBM).

Evaluation of Social Networks

The rising scale and intensity of social network use (the social network notion will hereafter be interchangeable with and abbreviated to the “net”), fueled by advertising companies, has highlighted the need for evaluating the net via its potential to influence and leverage the activity of net actors.The entities playing key roles in the nets are: owners, members, advertisement companies, producers, and consumers. Each of these entities has its own interests, which tend to be contradictory to or synergic with those of others. These interests can be summarized as the following types of interactions: - The owners strive to maximize profitability and cash generation by increasing the number of their members and by increasing the net’s member usage, inter alia, by promoting the net’s total time consumption, and thus enlarging the advertising impact. - The members strive to increase the circulation of their ideas among the net’s membership in the most user friendly and attractive manner: to minimize expenses (such as membership fees) and to reduce the likelihood of causing damage and disrupting privacy. - The advertisement companies supply material to be exposed to their members – for internalizing their idea consciously or subconsciously among the maximal number of suitable members.- The producers generally have long-range plans to sell their product to the maximal number of consumers with the minimal expenses. The advertisement companies are generally the “long arm” of the producers.- The consumers are generally interested in being exposed to innovations related to their fields of interest; however, they prefer not to be bothered by irrelevant advertisements (spam) and they refuse to pay directly for any advertisements (free riders). The members’ set is a subset of the consumers.The aim of the paper is to comprehensively analyze the notions presented above and to recommend the optimal approach for the presented issue for each of the above entities, considering various behaviors, some of which are mentioned below:- The message inserted in the advertisement is distributed to the members’ community in several iterations to potential consumers.- Generally, the members will agree to forego protected privacy for the free net. - The advertisement should be varied to catch the members' attention several times using the same message.There are three suggested main modes of challenging the net’s analysis in the above context: - Data Scope is an old method in which the data flow in the net is collected and analyzed online in real time.- The net’s specifications or parameters, such as the number of members, the average data flow frequency, and the average number of connections for each member's interactions, is formulated. - The simulation is useful for analyzing the net by predicting the additional net development and its steady-state rate.This article may enable a more accurate estimation of the net’s value by regulating and stabilizing the net's behavior and by avoiding unexpected downtime.

Scientific collaboration in biotechnology: the case of the northeast region in Brazil

The objective of this study is to investigate scientific collaboration in biotechnology in the northeast region of Brazil. The data presented refer to the 1980---2010 period and were collected from the Brazilian National Council for Scientific and Technological Development platform database known as Lattes (a compilation of curricula vitae of researchers in Brazil, including a record of their scientific production) and from the Institute for Scientific Information Web of Science database. Our analysis involved the use of bibliometric indicators, specifically co-authorship between or among institutions, as well as the evaluation of social networks and multivariate statistics. Overall, we verified that collaboration takes place mostly at the intra-institutional level. At intra-regional scale, we could observe the development of four clusters in relation to the collaboration dynamics, in which geographic proximity stands out as grouping factor. At the interregional level, the partnerships revolve around institutions that count with laboratory infrastructure and research tradition in the field of biotechnology. Regarding international collaboration, it remains connected to national scientific cooperation programs.

Candidate change agent identification among men at risk for HIV infection

Despite limited HIV prevention potency, peer-based programs have become one of the most often used HIV prevention approaches internationally. These programs demonstrate a need for greater specificity in peer change agent (PCA) recruitment and social network evaluation. In the present three-phase study based in India (2009–2010), we first explored the nature of friendship among truck-drivers, a group of men at high risk for HIV infection, in order to develop a thorough understanding of the social forces that contribute to and maintain their personal networks. This was accomplished in the first two study phases through a combination of focus group discussions (n = 5 groups), in-depth qualitative interviews (n = 20), and personal network analyses (n = 25) of truck-drivers to define friendship and deepen our understanding of friendship across geographic spaces. Measures collected in phases I and II included friend typologies, discussion topics, social network influences, advice-giving, and risk reduction. Outcomes were assessed through an iterative process of qualitative textual analysis and social network analysis. The networks of truck-drivers were found to comprise three typologies: close friends, parking lot friends, and other friends. From these data, we developed an algorithmic approach to the identification of a candidate PCA within a high-risk man's personal network. In phase III we piloted field-use of this approach to identify and recruit PCAs, and further evaluated their potential for intervention through preliminary analysis of the PCA's own personal networks. An instrument was developed to translate what social network theory and analysis has taught us about egocentric network dynamics into a real-world methodology for identifying intervention-appropriate peers within an individual's personal network. Our approach can be tailored to the specifications of any high-risk population, and may serve to enhance current peer-based HIV interventions.