Social Network Visualization Research Articles

A pilot test of a motivational interviewing social network intervention to reduce substance use among housing first residents

This article presents findings of a pilot test of a Motivational Interviewing social network intervention (MI-SNI) to enhance motivation to reduce high risk alcohol and other drug (AOD) use among formerly homeless individuals transitioning to housing. Delivered in-person by a facilitator trained in MI, this four-session computer-assisted intervention provides personalized social network visualization feedback to help participants understand the people in their network who trigger their alcohol and other drug (AOD) use and those who support abstinence. If ready, participants are encouraged to make changes to their social network to help reduce their own high-risk behavior. Participants were 41 individuals (33 male, 7 female, 1 other; 23 African-American, 5 non-Latino White, 6 Latino, 7 other, mean age 48) who were transitioning from homelessness to permanent supportive housing. They were randomly assigned to either the MI-SNI condition or usual care. Readiness to change AOD use, AOD abstinence self-efficacy, and AOD use were assessed at baseline and shortly after the final intervention session for the MI-SNI arm and around 3-months after baseline for the control arm. Acceptability of the intervention was also evaluated. MI-SNI participants reported increased readiness to change AOD use compared to control participants. We also conducted a subsample analysis for participants at one housing program and found a significant intervention effect on readiness to change AOD use, AOD abstinence self-efficacy, and alcohol use compared to control participants. Participants rated the intervention as highly acceptable. We conclude that a brief computer-assisted Motivational Interviewing social network intervention has potential to efficaciously impact readiness to change AOD use, AOD abstinence self-efficacy, and AOD use among formerly homeless individuals transitioning to permanent supportive housing, and warrants future study in larger clinical trials.

The study on the core personality trait words of Chinese medical university students based on social network analysis.

The medical university students in China whose school work is relatively heavy and educational system is long are a special professional group. Many students have psychological problems more or less. So, to understand their personality characteristics will provide a scientific basis for the intervention of psychological health.We selected top 30 personality trait words according to the order of frequency. Additionally, some methods such as social network analysis (SNA) and visualization technology of mapping knowledge domain were used in this study.Among these core personality trait words Family conscious had the 3 highest centralities and possessed the largest core status and influence. From the analysis of core-peripheral structure, we can see polarized core-perpheral structure was quite obvious. From the analysis of K-plex, there were in total 588 “K-2”K-plexs. From the analysis of Principal Components, we selected the 11 principal components.This study of personality not only can prevent disease, but also provide a scientific basis for students’ psychological healthy education. In addition, we have adopted SNA to pay more attention to the relationship between personality trait words and the connection among personality dimensions. This study may provide the new ideas and methods for the research of personality structure.

Impelling research productivity and impact through collaboration: a scientometric case study of knowledge management

A case study for impelling university research productivity and impact through collaboration is presented. Scientometric results support the hypothesis that a knowledge management model increased research collaboration and thereby boosted a university’s number of publications and citations. Results come from fifteen years of data at a Mexican university with 2400 researchers who produced 24,000 works in fifteen research disciplines. These data are treated with social network visualizations and algorithms to identify patterns of collaboration and clustering, as well as with normalizations to make disciplines comparable and to verify increasing citation impact. The knowledge management model implemented in the study may be a cost-effective way for universities to intensify collaboration and improve research performance.

On status epilepticus and pins: A systematic content analysis.

Status epilepticus (SE) can be defined as abnormally prolonged, persistent, or recurrent clinical and/or electrographic epileptic activity and, as such, is a challenging medical emergency requiring an aggressive treatment aimed at promptly terminating the seizures. It imposes a relevant clinical burden, both in terms of comorbidity and mortality. In the era of the Web 2.0, most people search the Web to obtain SE-related information. The current investigation aimed at qualitatively characterizing the pins related to SE: Pinterest, "the world's catalog of ideas", is a visual social networking site that enables users to freely upload visual material, to bookmark, and to share it (repin). Using SE as a keyword, 192 pins were extracted and analyzed on the basis of their content. Fifty-five were found to meet the inclusion criteria. Fifty-six point four percent of the pins reported at least one cause of SE, the most quoted of which being remote brain injuries (47.3% of the pins); 54.5% and 45.5% of the included pins reported SE symptoms and diagnosis, respectively; 72.7% and 40.0% of pins focused on SE treatment and on prognosis, respectively; and 50.9%, 30.9%, and 40.0% of the pins were intended for physicians, medical/nursing students, and lay people, respectively. Only 12.7% of pins were patient-centered and devoted to fund-raising and advocacy. In the field of neurological diseases, Pinterest, despite being a "pinstructive" tool, is too much overlooked and underused for advocacy purposes. Healthcare workers and stakeholders should be aware of the opportunities offered by Pinterest and exploit this visual social networking site for raising awareness of the life-threatening condition of SE, promoting fund-raising campaigns.

An Interactive, Mobile-Based Tool for Personal Social Network Data Collection and Visualization Among a Geographically Isolated and Socioeconomically Disadvantaged Population: Early-Stage Feasibility Study With Qualitative User Feedback.

BackgroundPersonal social networks have a profound impact on our health, yet collecting personal network data for use in health communication, behavior change, or translation and dissemination interventions has proved challenging. Recent advances in social network data collection software have reduced the burden of network studies on researchers and respondents alike, yet little testing has occurred to discover whether these methods are: (1) acceptable to a variety of target populations, including those who may have limited experience with technology or limited literacy; and (2) practical in the field, specifically in areas that are geographically and technologically disconnected, such as rural Appalachian Kentucky.ObjectiveWe explored the early-stage feasibility (Acceptability, Demand, Implementation, and Practicality) of using innovative, interactive, tablet-based network data collection and visualization software (OpenEddi) in field collection of personal network data in Appalachian Kentucky.MethodsA total of 168 rural Appalachian women who had previously participated in a study on the use of a self-collected vaginal swab (SCVS) for human papillomavirus testing were recruited by community-based nurse interviewers between September 2013 and August 2014. Participants completed egocentric network surveys via OpenEddi, which captured social and communication network influences on participation in, and recruitment to, the SCVS study. After study completion, we conducted a qualitative group interview with four nurse interviewers and two participants in the network study. Using this qualitative data, and quantitative data from the network study, we applied guidelines from Bowen et al to assess feasibility in four areas of early-stage development of OpenEddi: Acceptability, Demand, Implementation, and Practicality. Basic descriptive network statistics (size, edges, density) were analyzed using RStudio.ResultsOpenEddi was perceived as fun, novel, and superior to other data collection methods or tools. Respondents enjoyed the social network survey component, and visualizing social networks produced thoughtful responses from participants about leveraging or changing network content and structure for specific health-promoting purposes. Areas for improved literacy and functionality of the tool were identified. However, technical issues led to substantial (50%) data loss, limiting the success of its implementation from a researcher’s perspective, and hindering practicality in the field.ConclusionsOpenEddi is a promising data collection tool for use in geographically isolated and socioeconomically disadvantaged populations. Future development will mitigate technical problems, improve usability and literacy, and test new methods of data collection. These changes will support goals for use of this tool in the delivery of network-based health communication and social support interventions to socioeconomically disadvantaged populations.

Identificación de áreas temáticas científicas mediante el análisis de co-palabras. El caso de la Universidad de La Frontera, Chile

El artículo presenta una experiencia de análisis y gestión de información científica y tecnológica para la focalización de la Investigación, Desarrollo e Innovación (I+D+i) en la Universidad de La Frontera, Chile. El objetivo fue identificar y visualizar áreas temáticas de la producción científica de la universidad visibles en el sistema de publicación científica y en el sistema de financiamiento público. La muestra consideró publicaciones y proyectos disponibles durante el periodo 2007-2011. La identificación de las áreas temáticas se aborda desde la utilizacióncomplementaria del método de las palabras asociadas y la validación de expertos, mediante el análisis temático de contextos elementales y la utilización de herramientas de visualización de redes sociales. Se extraen aprendizajes de gestión de información y gestión de capacidades científicas y tecnológicas.This paper presents an experience of the analysis and management of scientific and technological information for directing research, development and innovation efforts in the Universidad de La Frontera. The study’s objective is to identify and envision researchfields in the University`s scientific production appearing in published scientific research and recorded in the public financing systems. The sample includes publications and projects published between 2007 and 2011. The task of research field identification is approached through the complementary use of “Cowords Analysis”; “Expert Validation Methodology” and “Thematic Analysis of Elementary Contexts”; in conjunction with social network visualization tools.Information regarding the management of information and scientific and technological capabilities are among the study’s findings.

How Instagram content affects brand attitudes and behaviour

This paper examines the effectiveness of communication on Instagram, a type of visual social networking site, by the US Transportation Security Administration (TSA). Results show that the TSA's Instagram account elicited stronger emotional reactions than a private consumer product business Instagram account. More importantly, perceived usefulness of content, perceived persuasive intent of content and negative emotions all affected attitudes toward the TSA. Additionally, perceived usefulness of content and negative emotions also influenced communicative action regarding the TSA account. Findings demonstrate the emotional impact of visual communication and the role of both cognitive and affective evaluations in changing attitudes and behaviour regarding a government agency.

Discovering the interdisciplinary nature of Big Data research through social network analysis and visualization

Big Data is a research field involving a large number of collaborating disciplines. Based on bibliometric data downloaded from the Web of Science, this study applies various social network analysis and visualization tools to examine the structure and patterns of interdisciplinary collaborations, as well as the recently evolving overall pattern. This study presents the descriptive statistics of disciplines involved in publishing Big Data research; and network indicators of the interdisciplinary collaborations among disciplines, interdisciplinary communities, interdisciplinary networks, and changes in discipline communities over time. The findings indicate that the scope of disciplines involved in Big Data research is broad, but that the disciplinary distribution is unbalanced. The overall collaboration among disciplines tends to be concentrated in several key fields. According to the network indicators, Computer Science, Engineering, and Business and Economics are the most important contributors to Big Data research, given their position and role in the research collaboration network. Centering around a few important disciplines, all fields related to Big Data research are aggregated into communities, suggesting some related research areas, and directions for Big Data research. An ever-changing roster of related disciplines provides support, as illustrated by the evolving graph of communities.

Visualization of Potential Social Network Focusing on Common Recognition of Small Scale Distributed Geothermal Power Plant Stakeholders in Beppu City, Oita Prefecture

Visualization of Potential Social Network Focusing on Common Recognition of Small Scale Distributed Geothermal Power Plant Stakeholders in Beppu City, Oita Prefecture

Forging a link between mentoring and collaboration: a new training model for implementation science.

BackgroundTraining investigators for the rapidly developing field of implementation science requires both mentoring and scientific collaboration. Using social network descriptive analyses, visualization, and modeling, this paper presents results of an evaluation of the mentoring and collaborations fostered over time through the National Institute of Mental Health (NIMH) supported by Implementation Research Institute (IRI).MethodsData were comprised of IRI participant self-reported collaborations and mentoring relationships, measured in three annual surveys from 2012 to 2014. Network descriptive statistics, visualizations, and network statistical modeling were conducted to examine patterns of mentoring and collaboration among IRI participants and to model the relationship between mentoring and subsequent collaboration.ResultsFindings suggest that IRI is successful in forming mentoring relationships among its participants, and that these mentoring relationships are related to future scientific collaborations. Exponential random graph network models demonstrated that mentoring received in 2012 was positively and significantly related to the likelihood of having a scientific collaboration 2 years later in 2014 (p = 0.001). More specifically, mentoring was significantly related to future collaborations focusing on new research (p = 0.009), grant submissions (p = 0.003), and publications (p = 0.017). Predictions based on the network model suggest that for every additional mentoring relationships established in 2012, the likelihood of a scientific collaboration 2 years later is increased by almost 7 %.ConclusionsThese results support the importance of mentoring in implementation science specifically and team science more generally. Mentoring relationships were established quickly and early by the IRI core faculty. IRI fellows reported increasing scientific collaboration of all types over time, including starting new research, submitting new grants, presenting research results, and publishing peer-reviewed papers. Statistical network models demonstrated that mentoring was strongly and significantly related to subsequent scientific collaboration, which supported a core design principle of the IRI. Future work should establish the link between mentoring and scientific productivity. These results may be of interest to team science, as they suggest the importance of mentoring for future team collaborations, as well as illustrate the utility of network analysis for studying team characteristics and activities.Electronic supplementary materialThe online version of this article (doi:10.1186/s13012-016-0499-y) contains supplementary material, which is available to authorized users.

Bridging the Gap between LMS and Social Network Learning Analytics in Online Learning

Despite the great potential of social network analysis (SNA) methods and visualizations for learning analytics in computer-supported collaborative learning (CSCL), these approaches have not been fully explored due to two important barriers: the scarcity and limited functionality of built-in tools in Learning Management Systems (LMS), and the difficulty to import educational data from formal virtual learning environments into social network analysis programs. This study aims to cover that gap by introducing GraphFES, an application and web service for extraction of interaction data from Moodle message boards and generation of the corresponding social graphs for later analysis using Gephi, a general purpose SNA software. In addition, this paper briefly illustrates the potential of the combination of the three systems (Moodle, GraphFES and Gephi) for social learning analytics using real data from a computer-supported collaborative learning course with strong focus on teamwork and intensive use of forums.

A study on small-world characteristics of Chinaäó»s high-speed railway networks

The small-world model provides a useful perspective and method to study the topological structure and intrinsic characteristics of high-speed rail networks (HRNs). In this paper, the P-space method is used to examine global and local HRNs in China, meanwhile the adjacency matrix is developed, then the social network analysis and visualization tool UCINET is used to calculate the spatial and attribute data of HRNs at national and local levels in China. The small-world characteristics of whole HRNs are discussed, three networks which have different properties are determined, and a comparative analysis of the small-world effect is detected. Then, the relationship between the construction of high-speed rail and regional development of China is analysed. The results show that: 1) China's HRNs have small average path length ( L ) and large clustering coefficient (C ), representing a typical small-world network; 2) Local HRNs have a certain correlation with economic development. The reasons for the difference of HRNs with respect to characteristics among regions are eventually discussed.

A semantic similarity measure for linked data: An information content-based approach

Linked Data allows structured data to be published in a standard manner so that datasets from diverse domains can be interlinked. By leveraging Semantic Web standards and technologies, a growing amount of semantic content has been published on the Web as Linked Open Data (LOD). The LOD cloud has made available a large volume of structured data in a range of domains via liberal licenses. The semantic content of LOD in conjunction with the advanced searching and querying mechanisms provided by SPARQL has opened up unprecedented opportunities not only for enhancing existing applications, but also for developing new and innovative semantic applications. However, SPARQL is inadequate to deal with functionalities such as comparing, prioritizing, and ranking search results which are fundamental to applications such as recommendation provision, matchmaking, social network analysis, visualization, and data clustering. This paper addresses this problem by developing a systematic measurement model of semantic similarity between resources in Linked Data. By drawing extensively on a feature-based definition of Linked Data, it proposes a generalized information content-based approach that improves on previous methods which are typically restricted to specific knowledge representation models and less relevant in the context of Linked Data. It is validated and evaluated for measuring item similarity in recommender systems. The experimental evaluation of the proposed measure shows that our approach can outperform comparable recommender systems that use conventional similarity measures.

Module-based visualization of large-scale graph network data

The efficient visualization of dynamic network structures has become a dominant problem in many big data applications, such as large network analytics, traffic management, resource allocation graphs, logistics, social networks, and large document repositories. In this paper, we present a large-graph visualization system called ModuleGraph. ModuleGraph is a scalable representation of graph structures by treating a graph as a set of modules. The main objectives are: (1) to detect graph patterns in the visualization of large-graph data, and (2) to emphasize the interconnecting structures to detect potential interactions between local modules. Our first contribution is a hybrid modularity measure. This measure partitions the cohesion of the graph at various levels of details. We aggregate clusters of nodes and edges into several modules to reduce the overlap between graph components on a 2D display. Our second contribution is a k-clustering method that can flexibly detect the local patterns or substructures in modules. Patterns of modules are preserved by the ModuleGraph system to avoid information loss, while sub-graphs are clustered as a single node. Our experiments show that this method can efficiently support large-scale social and spatial network visualization. Graphical Abstract text

Marijuana-Related Posts on Instagram.

Instagram is a highly visual social networking site whose audience continues to grow, especially among young adults. In the present study, we examine marijuana-related content on Instagram to better understand the varied types of marijuana-related social networking occurring on this popular social media platform. We collected 417,561 Instagram posts with marijuana-related hashtags from November 29 to December 12, 2014. We assessed content of a random sample (n = 5000) of these posts with marijuana-related hashtags. Approximately 2136 (43%) were explicit about marijuana and further analyzed. Of the 2136 marijuana-related posts, images of marijuana were common (n = 1568). Among these 1568 marijuana images, traditional forms (i.e., buds/leaves) were the most common (63%), followed by some novel forms of marijuana, including marijuana concentrates (20 %). Among the 568 posts that displayed marijuana being ingested, 20% showed someone dabbing marijuana concentrates. Marijuana-related advertisements were also observed among the 2136 marijuana-related posts (9 %). Our findings signal the promotion of marijuana use in its traditional plant-based form; trendy and novel modes of marijuana ingestion were also endorsed. This content along with the explicit marketing of marijuana that we observed on Instagram have potential to influence social norms surrounding marijuana use.

A computer-assisted motivational social network intervention to reduce alcohol, drug and HIV risk behaviors among Housing First residents.

BackgroundIndividuals transitioning from homelessness to housing face challenges to reducing alcohol, drug and HIV risk behaviors. To aid in this transition, this study developed and will test a computer-assisted intervention that delivers personalized social network feedback by an intervention facilitator trained in motivational interviewing (MI). The intervention goal is to enhance motivation to reduce high risk alcohol and other drug (AOD) use and reduce HIV risk behaviors.Methods/designIn this Stage 1b pilot trial, 60 individuals that are transitioning from homelessness to housing will be randomly assigned to the intervention or control condition. The intervention condition consists of four biweekly social network sessions conducted using MI. AOD use and HIV risk behaviors will be monitored prior to and immediately following the intervention and compared to control participants’ behaviors to explore whether the intervention was associated with any systematic changes in AOD use or HIV risk behaviors.DiscussionSocial network health interventions are an innovative approach for reducing future AOD use and HIV risk problems, but little is known about their feasibility, acceptability, and efficacy. The current study develops and pilot-tests a computer-assisted intervention that incorporates social network visualizations and MI techniques to reduce high risk AOD use and HIV behaviors among the formerly homeless.ClinicalTrials.gov IdentifierNCT02140359.

Exploring associations between resilience and construction safety performance in safety networks

Safety management plays a major role in construction as negligence may result in loss of lives and detrimental project consequences. The Middle East witnesses poor safety management where companies need to improve their performance instead of hiding their deficiencies. This study aims at evaluating safety performance and network resilience to risks by studying safety-related interactions among the construction team. Safety on three mega-projects is evaluated by analyzing their respective networks for communication and safety management and mapping them using Gephi. Using Social Network Analysis (SNA), visualizations and various metrics were computed revealing network characteristics and communication patterns. Resilience metrics were measured using actual safety performance data and overall network resilience was simulated through agent-based modeling using NetLogo. Results and correlations show that networks with better interaction and structure have higher resilience to prevalent risks and have better actual safety performance.

Efficient visualization of social networks based on modified Sammon׳s mapping

Visualization is an important part of Network Analysis. It helps to find features of the network that are not easily identifiable. In this paper, we present a novel approach to the visualization of weighted networks based on the Distance Geometry Problem. The network may be seen as a set of data points in space induced by the incidence relation or as a symmetric matrix of vertex distances. We propose two methods for construction of the input for Sammon׳s mapping and discuss the effect of the particular methods on the final layout. In this work, we use Differential Evolution as a real-parameter optimization metaheuristic algorithm to minimize the error function used in Sammon׳s mapping. The presented experiments used the well-known Zachary׳s Karate Club network and weighted co-authors network based on the DBLP database. We present our approach to the visualization of weighted networks based on Sammon׳s mapping and linear approximation. Dimensionality reduction and graph based visualization methods can uncover hidden structures of high dimensional data and visualize it in a low-dimensional vector space.

From Big Noise to Big Data: Toward the Verification of Large Data sets for Understanding Regional Retail Flows

There has been much excitement among quantitative geographers about newly available data sets, characterized by high volume, velocity, and variety. This phenomenon is often labeled as “Big Data” and has contributed to methodological and empirical advances, particularly in the areas of visualization and analysis of social networks. However, a fourth v—veracity (or lack thereof)—has been conspicuously lacking from the literature. This article sets out to test the potential for verifying large data sets. It does this by cross‐comparing three unrelated estimates of retail flows—human movements from home locations to shopping centers—derived from the following geo‐coded sources: (1) a major mobile telephone service provider; (2) a commercial consumer survey; and (3) geotagged Twitter messages. Three spatial interaction models also provided estimates of flow: constrained and unconstrained versions of the “gravity model” and the recently developed “radiation model.” We found positive relationships between all data‐based and theoretical sources of estimated retail flows. Based on the analysis, the mobile telephone data fitted the modeled flows and consumer survey data closely, while flows obtained directly from the Twitter data diverged from other sources. The research highlights the importance of verification in flow data derived from new sources and demonstrates methods for achieving this.

Mapping civil society with social network analysis: Methodological possibilities and limitations

This paper explores the possibility of using social network analysis and visualization as a tool for qualitative research in human geography. The approach uses formal network analysis in concert with ethnographic research methods. Specifically, we take a performative approach to network analysis that sees network visualization as a process that produces space for research. Using networks of civil society organizations as our example, this paper highlights the debates over what social network analysis allows and omits, focusing in particular on issues related to flows, power, boundary demarcation and abstraction. From a methodological perspective, much can be lost when the conceptual and theoretical arguments about networks are applied to the material and embodied practices that constitute network relations. Nevertheless, the formal analysis of such networks can provide a representation of relationships at a moment in time that can help to both express those relationships and to open new questions that can be explored using other methods. Just as abstraction is used in an iterative process to move between empirical evidence and conceptual and theoretical arguments, the representation of networks can be part of a methodological approach that moves between the representation of relationships and the ways that various agents express, experience, and remake those relationships. Using the example of research on NGOs and civil society organizations promoting citizenship for young people in divided societies, we explore the utility – and limitations – of working in the liminal space of formal network analysis and more ethnographic approaches.