Social Network Analysis Techniques Research Articles

Connectivity Analysis of Non-Container Ports at PT Pelindo Multi Terminal Using SNA and GIS / Connectivity Analysis of Non-Container Ports at PT Pelindo Multi Terminal Using SNA and GIS

This study aims to analyze the connectivity of non-containerized ports, particularly in the sub-commodity of vegetable/animal oils and fats, within the operational area of PT Pelindo Multi Terminal. Using Social Network Analysis (SNA) and Geographic Information System (GIS) techniques, this study builds a network based on nodes (ports) and assesses inter-node connections with metrics such as degree centrality, betweenness centrality, and hub index. Gephi application was used for the calculation of network metrics, while QGIS visualized the shipping routes. The research revealed that high traffic ports such as Belawan, Dumai, and Tanjung Priok play a crucial role in the network. The findings support recommendations for infrastructure development and implementation of an integrated Terminal Operations System (TOS) to improve logistics efficiency.

Network Issue Agenda Setting on Facebook: Exploring the Interplay Between Polarized Campaigns and Party Supporters

This study undertook an analysis of network agenda setting during the 2020 U.S. Presidential campaign, focusing on the interactions between the campaigns and their respective supporters within the context of a polarized social media environment. By employing social network analysis techniques to examine issue agendas, the study revealed a relatively weak correlation between the agendas of the campaigns and their affiliated supporters on Facebook. Conversely, it found a notable association between entities sharing the same ideological orientation—party supporters displayed a higher degree of engagement with their own party’s campaign, and vice versa. The implications of these findings, from a theoretical, methodological, and practical standpoint, have been thoroughly discussed.

Research on provincial implied carbon emissions in China under the shared responsibility driven by new quality productivity: A new approach

The allocation of responsibility for carbon emissions among involved parties has garnered significant attention. This study proposes a comprehensive sharing coefficient methodology known as CVAT (direct carbon emissions, value added, and technology level) to enhance the accuracy and equity of responsibility sharing driven by the new productivity standard. This study utilizes the environmental expansion multi-region input-output (EEMRIO) model to assess China's implicit provincial carbon emissions over the previous two decades. The study investigates the characteristics of the implied carbon transfer network using social network analysis (SNA) techniques. Key findings include: (1) The CVAT method's determination of responsibility sharing coefficients aligns with fairness and efficiency principles, resulting in a more equitable distribution of implied carbon emission responsibilities conducive to achieving fair carbon emission reduction. (2) Over the research period, national implied carbon emissions exhibited an upward trajectory, increasing from 3.305 billion tons in 2002 to 9.726 billion tons in 2017, with Shandong, Hebei, and Jiangsu provinces ranking among the top emitters in China. (3) Eastern coastal and western regions function as net carbon transfer out regions, while the central, northeast, and southwest regions act as net carbon transfer in regions. (4) The implied carbon transfer network demonstrated relative stability throughout the study period, with Shandong, Hebei, Jiangsu, and Inner Mongolia emerging as major carbon transfer provinces. The study's outcomes hold significant implications for the impartial and accurate assessment of carbon emission responsibilities and the advancement of global carbon emission reduction efforts.

A Bibliometric Analysis Of Emotional Intelligence And Leadership

This research aims to conduct a bibliometric analysis of studies on the relationship between emotional intelligence and leadership. The investigation encompasses a compilation of 1136 articles sourced from Scopus, spanning from 1996 to 2023. The data analysis process involved utilizing the VOSviewer software, which facilitated the creation of maps depicting co-authorship and keywords. A combination of descriptive statistics and social network analysis techniques were employed to analyze the data. Within the 1136 articles, 105 author keywords were referenced at least five times. These keywords were subsequently categorized into 11 clusters. The prevalent terms included “training,” “competence,” “job satisfaction,” “self-awareness,” and “organizational citizenship behavior.” An exploration of the temporal aspect of the data revealed that recent scholarly interest has gravitated toward topics such as “higher education,” “job performance,” “leader development,” “soft skill,” and “employee engagement.” This study’s findings suggest potential avenues for further research in the realm of emotional intelligence and leadership. Subsequent researchers might consider conducting studies in these domains to contribute to the evolving knowledge system.

Personal Networks Supporting Workplace Learning

This paper is a case study researching personal social networks and their meaning to individual-level learning processes in the workplace. The study was based on the knowledge-creation metaphor of learning. Employees' personal networks are seen as an important component of their learning potential and competence. Methodologically, the paper presents a relatively new technique of Social Network Analysis (SNA), namely, the qualitative egocentric network interview, and a new way of presenting research findings in visual form. The context of the study is the Finnish Defence Forces (FDF) and it was conducted in three companies of one brigade-level unit of the Finnish Army. An egocentric network interview was conducted with ten Non-Commissioned Officers (NCO). The interview data was analysed with qualitative content analysis, and the networks were visualised with the Cytoscape software. The egocentric network analysis showed that the people in the same company created a major support structure for the NCOs’ workplace learning. However, nearly all NCOs had important network structures that were formed around their individual expertise and tasks. The networks varied considerably in size and composition, but had certain connecting features. The networks had three main components; one, the personnel of the NCO’s own unit provided important social support. Two, every NCO had networks related to their own specific task, and three, some NCOs had networks formed through various stages of their life that were still active and useful in their current job. The different ways in which the networks enable and support workplace learning are discussed. In addition, some methodological issues of social network analysis are addressed.

A Panoramic View on International Higher Education Studies from a Global Perspective

With globalization, international education has become much more accessible to more people around the world. International higher education (IHE) has been among the most studied topics in higher education in recent years. In this context, the present study aims to investigate the studies conducted on IHE through a systematic literature review by analyzing studies published between 1965 and 2021 and employs social network analysis (SNA) and text-mining techniques. All these findings show that global, national, and institutional strategies are effective in the increase of interest in international education and especially in the increase of research. In this connection, this study provides an overview of trends; academic production in IHE has been researched with the data mining and analytics approaches, bibliometric method and the analysis of social networks. Thus, a better understanding and interpretation of the leading themes in the literature of internationalization in higher education is possible to achieve a better understanding of the field of intellectual scientific knowledge in the literature on globalization and IHE.

The influence of service performance in China's sci-tech commissioner system: Using social network analysis and interpretable machine learning

The Sci-Tech Commissioner System (SCS) is a result of exploratory efforts by the Chinese government to use science and technology to strengthen the agricultural sector. Social network analysis (SNA) and machine learning (ML) techniques make it feasible to assess the service performance in China's SCS by using indicators such as group types and structure features. In this study, SNA and a clustering algorithm were employed to categorize service group types of sci-tech commissioners. By comparing the accuracy of different classification algorithms in predicting the clustering results, LightGBM algorithm was finally select to determine the clustering features of sci-tech commissioners and establish an interpretable ML model. Then, the SHAP was used to algorithm to analyze influences affecting service performance. Results show that the service forms of sci-tech commissioners are group-oriented, and that group types include small groups of young commissioners with close cooperation, larger groups of young and middle-aged commissioners, small groups of middle-aged and old commissioners with close cooperation, and isolated points of highly-influential commissioners. Furthermore, while group size is not the determinant of a commissioner's average performance, group structure and coordination ability were found to be more critical. Moreover, while differences in distinct types of service performance are caused by various factors, but good group structures and extensive social contacts are essential for high service performance.

Mapping social movements momentum: unveiling networks in the movement for the right to abortion in Mexico

In this case study, we propose a network analysis approach to map social movements through an intersectional feminist lens. We aim to gain a more in-depth understanding of movements' network structures to examine the roles and relationships of movement actors, the flow of resources between them, and potential areas of collaboration and conflict. By incorporating Social Network Analysis (SNA) techniques and visually mapping dynamics within social movements, this approach can assess the significance of small actors in creating change and stresses the need for their perspectives to be heard. Furthermore, our methodology adopts a feminist intersectional framework that recognizes the role of different identities in shaping the movement and its actors. To demonstrate the practical application of this approach, we examined the movement for the right to abortion in Mexico between September and December 2021. Our multi-step process included (1) designing a survey tool adapted to the context of the movement, (2) collecting and analyzing survey responses on movement actors' relationships and interactions, including their priorities, activities, needs, risks, and challenges, (3) visualizing the network using SNA techniques that account for the complexity and diversity of the movement, (4) interpreting the findings through insights collected in semi-structured interviews and validation meetings with key movement actors, and (5) implementing safety and security guidelines to safeguard the identity of individuals whose activities could put them at risk of suffering institutional, emotional, and physical violence. Our case study offers valuable insights that not only encourage the integration of feminist and intersectional perspectives into data collection processes but also provide a roadmap for accompanying social movements and supporting meaningful and contextually responsive activities.

The BTS ARMY on Twitter flocks together: How transnational fandom on social media build a viable system

The world’s largest transnational fandom community of the BTS (BangTan Sonyeondan) ARMY (Adorable Representative M.C. for Youth) is structured to achieve common goals and exert influence. To identify a mechanism that explains the organizational structure of transnational virtual communities, we empirically investigate how the global fandoms carry out autonomous activities and adapt to the environment without coercive governance despite their massive size and complexity. Using the techniques of social network analysis and viable system model, we found some structural differences between the online communities with a viable and non-viable structure. First, the BTS ARMY network is sustained by a systematic structure of key players who voluntarily play distinct roles. In contrast, the Arianator network is relatively unorganized and dispersed. Moreover, multi-fandom accounts can act as potential user acquisition roles. Finally, two different diffusion types, text-based information and image-based media, are carried by distinct roles of subsystems. As one of the first studies to empirically examine the viability of online transnational fandom from an organizational structure perspective, this paper provides practical implications for fandoms from three perspectives; (1) resource mobilization and sustainability, (2) influence of fandom dynamics, and (3) economic impact. The results suggest that fandoms taking strategic actions upon the viable structure on digital platforms could represent a new cultural hegemony, promoting global unity and pop cosmopolitanism.

Assessing the EU Climate and Energy Policy Priorities for Transport and Mobility through the Analysis of User-Generated Social Media Content Based on Text-Mining Techniques

For over three decades, the European Union’s (EU) transport policy has aimed at fostering environmental sustainability and energy efficiency. Since 2015, European policymakers have focused more on three key sustainable development goals: decarbonizing the transport system, promoting low-emission mobility solutions, and transitioning to renewable and alternative fuels. To effectively communicate priorities and engage stakeholders, EU policymakers regularly use social media platforms like Twitter (now known as X). This active discourse involves policymakers, industrial stakeholders, the media, and the public, offering insights into the role of transport policy in addressing climate change and energy transition challenges. The current research endeavors to track and analyze the evolution of user-generated content related to climate change, energy transition, and smart mobility on Twitter from 2011 to 2021. This research uses text-mining and social network analysis techniques to quantitatively and qualitatively assess the dynamics of relevant EU policies and their effects. The study’s findings can be used to establish a robust monitoring and evaluation framework at the EU and national levels. This framework will assess the effectiveness of communicating strategic priorities for sustainable transport development. It also holds potential for application in other sectors, broadening its impact.

Spatial association of coastal towns and villages in Funing District, Qinhuangdao: A study using social network analysis

This study aims to explore how the spatial network structure between towns and villages can be optimized, which is a crucial element of rural revitalization strategies. For this investigation, we choose Funing District in Qinhuangdao, situated along the coastal region of the Bohai Sea. To achieve this, a gravity model is constructed through integration and collection of data, then applied social network analysis (SNA) techniques. Empirical analysis is conducted using ArcGIS and the NetworkX library in Python to uncover essential aspects of the town-village network in Funing District, including overall network density, centrality, cohesive subgroups, and core-periphery structures. The results of this research reveal a core-periphery structure in the town-village network of Funing District, highlighting significant hierarchical differences among nodes regarding spatial connectivity. With these findings in mind, specific measures are proposed to improve the flow of elements between nodes and optimize the spatial network structure. These measures encompass enhancing the strategic position of urban nodes, endorsing rural revitalization initiatives, and actively advocating for the establishment of a spatial layout embodying "one core, three stars, four guards, one axis and one belt" to facilitate resources and elements integration between urban and rural locales. Furthermore, this study offers valuable insights and references for town-village planning and development across the Bohai Rim region, encompassing the Beijing-Tianjin-Hebei area and analogous regions.

Art Crime Does not pay: Multiplexed Social Network Analysis in Cultural Heritage Trafficking Forensics

Nowadays, crimes connected to cultural heritage can feature as a staple for organised crime networks and act as financial enablers for international conflicts, including terrorism organisations and even inter-state conflicts, in several ways. Goods of cultural significance include a range of valuable objects related to human cultures, like works of art, historical artefacts, and other antiques, but also forgeries based on such objects. These crimes are almost always transnational, for instance, involving theft or looting in one country and goods moved across borders to be sold. This article presents an intelligence methodology based on Social Network Analysis (SNA) techniques that can support law enforcement agencies (LEAs) in their daily struggle against criminals that also pose a threat to national security. The methodology proposed is based on the building of a blended, multiplexed social network graph, deriving from the fusion of a diverse set of data sources, both in the open-source domain (OSINT) and in the classified domain. We will present data collection methods, correlation between sources, possible ways to generate blended links between individuals that retain information from different sources, and SNA techniques applied to intelligence and investigations. The article provides an answer to the following research questions: how we can detect and identify criminal activities and networks related to cultural goods crimes, how we can assist LEAs in countering illicit trafficking, and how we can ensure that art crime does not pay.

Research on Mixed Education Teaching Reform in the Era of Internet plus

Nowadays, the technology of "Internet plus" is developing very rapidly. Its cooperation with education promotes the innovative development of educational reform practice, improves the vitality of education. The cooperation of online and offline education has innovated teaching ideas and changed the teaching situation. It fully enhances students' proactive practical skills, thereby improving their comprehensive quality level, developing new educational concepts, and promoting innovation in new teaching models. Tourism behavior prediction based on social network analysis employs social network analysis techniques to predict the behavior patterns of tourists in different stages of their travel, such as destination selection, itinerary planning, and activity participation. By analyzing the behavior patterns of tourists on social media platforms, social network analysis algorithms can identify the factors that influence their decision-making, such as personal preferences, social influence, and situational context, and generate personalized recommendations to improve their travel experience. For example, if a tourist has shown a preference for outdoor activities on social media, tourism behavior prediction systems can recommend relevant activities and destinations that match their interests. These systems can also incorporate real-time data, such as weather forecasts and traffic conditions, to adjust recommendations accordingly. Overall, tourism behavior prediction based on social network analysis can provide tourists with more personalized and satisfactory travel experiences, while also promoting the development of local tourism industries.

Exploring the backward and forward linkages of production network in a developing country

This paper applies social network analysis (SNA) techniques to the input–output table in order to investigate the production network of Pakistan’s economy. Through different network measures, it identifies the sectors that exert significant direct and indirect impacts on other sectors. The findings reveal that, overall, sectors have weak forward linkages relative to backward linkages. The services sectors, particularly transportation and trade services, have the highest out-degree (a measure of forward linkages). In contrast, the manufacturing sectors demonstrate comparatively lower connectivity than the services sectors. The sectors of electricity, petroleum, and chemicals emerge as the most widely utilized inputs across various industries. Despite their relatively moderate immediate absorption in downstream industries, these sectors possess the potential to affect other industries indirectly. Considering the combined measures of network analysis, these sectors also emerge as the most crucial sectors in Pakistan’s production network. This suggests that enhancing efficiency in the energy, refining, and petrochemical industries will yield the highest economy-wide impact.

Integrating tourists’ walk and talk: a methodological approach for tracking and analysing tourists’ real behaviours for more sustainable destinations

This study provides a methodological contribution in the analysis of tourists’ sustainable behaviour. Traditional methods encounter challenges in addressing the attitude-behaviour gap, prompting the need for new approaches. Using diverse big data sources, the paper introduces a novel methodology for analysing real behaviour patterns, extending Social Network Analysis techniques. The aim is to identify sustainable behaviours that ease overcrowding, distribute visitor flows, and optimize economic diversification. The methodology is valuable for local authorities in monitoring tourist behaviours, aiding in informed decision-making for a more sustainable approach to tourism.

Deconstructing cultural appropriation in online communities: A multilayer network analysis approach

In this study, we introduce a novel multilayer network model designed to analyze complex social phenomena in online communities. The model captures intricate relationships between users, content, and specific aspects of social phenomena, providing a comprehensive framework for understanding these interactions. We applied this model to a dataset of over 1 million Reddit comments from January to April 2022, filtered for cultural appropriation-related keywords. Our quantitative analyses, based on Social Network Analysis techniques, revealed significant findings. For instance, a subreddit exhibited the highest user interaction, indicating a substantial level of engagement on this topic. Furthermore, the distribution of key contents across different subreddits was non-uniform, suggesting diverse levels of engagement across communities. The results of this research underscore the potential of our approach in providing a nuanced understanding of social phenomena in online communities, thereby contributing to future research in this field.

A network analysis-based framework to understand the representation dynamics of graph neural networks

In this paper, we propose a framework that uses the theory and techniques of (Social) Network Analysis to investigate the learned representations of a Graph Neural Network (GNN, for short). Our framework receives a graph as input and passes it to the GNN to be investigated, which returns suitable node embeddings. These are used to derive insights on the behavior of the GNN through the application of (Social) Network Analysis theory and techniques. The insights thus obtained are employed to define a new training loss function, which takes into account the differences between the graph received as input by the GNN and the one reconstructed from the node embeddings returned by it. This measure is finally used to improve the performance of the GNN. In addition to describe the framework in detail and compare it with related literature, we present an extensive experimental campaign that we conducted to validate the quality of the results obtained.

Social Network Analysis as a Driver of Continuous Improvement

Social network analysis (SNA) is a research method that, when applied to improvement science, can help leaders understand the strength of relationships within their organization. The COVID-19 pandemic has had a lasting impact on organizational norms, and it has interrupted relationship building efforts. This paper documents a case study of the Kentucky Department of Education (KDE), which deployed SNA techniques to strategically identify areas of growth within its network and design intentional, targeted solutions to improve the network health. As organizations emerge from the pandemic environment and begin to plan continuous improvement efforts, they would be well served to examine the impact of the pandemic on their level of connectedness. The broader impact and generalizability of the case study as well as considerations for replication are also discussed.

Corporate Networks and Business Groups in Egypt, 1924–1948: Economic Necessity or Entrepreneurial Dynamism

By providing fresh insights into the development of Egyptian corporate networks and business groups between the early 1920s and late 1940s, this article extends the geographical ambit of an expanding field of study. Data from contemporaneous sources were analyzed through social network analysis techniques, in the search for an improved understanding of both the nature of and motivations behind networking in Egypt. In conducting this exercise, this article assesses the success with which the state acted to stimulate local entrepreneurship in its search for greater economic independence. By linking the Egyptian case to a wider literature on corporate networks, the article reveals similarities with other cases of how developing economies responded to the challenges of that era.

Interactive mapping of Covid-19 disinformation in Ibero-America

Fake news, created with different intentions and disseminated through various social networks and messaging platforms, has become a significant means of disinformation in the current landscape. In the face of certain events or controversial situations, the number of fake new items increases, a situation that can become worrying. One such event was the arrival of the Covid-19 pandemic. To combat this phenomenon, various fact-checking bodies appeared with the objective of demonstrating and disseminating whether a certain piece of information was correct. At the international level, these organizations have joined together in the #CoronaVirusFacts/#DatosCoronaVirus Alliance. The organization Chequeado from Argentina has collected information coming from Latin American (Latam) countries plus Spain and Portugal in a specific database, which allows for their analysis on an independent basis. The objective of this work is to analyze and make graphically visible the thematic distribution, media typology, and digital formats of the fake news that circulated regarding the coronavirus pandemic in Ibero-America by means of the social network analysis technique (SNA) and others, using the information from the aforementioned database. Its final visualization is shown by means of interactive mapping, which can be manipulated by the user to analyze the results through the application of different integrated filtering strategies.