Social Network Analysis Methods Research Articles

Penerapan dan Analisis Metode Social Network Analysis untuk Menentukan Rekomendasi Diskon dengan Menggunakan Library NetworkX

At present technology has developed rapidly, especially in industrial technology. Business competition is getting tighter, making people have to find ways to maintain their business. One strategy that can be used is to provide discounts on goods or services sold. This discount can be made based on the company's sales data or other factors. In this case, the company has large sales data. Therefore, a way is needed to process the large sales data, and one efficient way to do this is to use the Social Network Analysis method. This study measures the degree of Information Centrality from the Social Network Analysis method to measure how much the item is purchased, this can be taken into consideration to determine discounts on goods or services sold. This research provides the results of discount recommendation applications that can process large sales data and provide recommendations based on the Social Network Analysis method.

Toward dual carbon targets: Spatial correlation on comprehensive carbon emission index in urban agglomerations based on a new evaluation model

China is confronted with substantial pressure to mitigate its carbon emissions. Urban agglomerations assume a pivotal role in the attainment of carbon peaking and carbon neutrality. This study constructed a carbon emission evaluation model from the perspectives of total quantity, intensity, efficiency, population, and land. A modified gravity model and social network analysis method was used to analyze the spatial correlation characteristics of six urban agglomerations, clarified their carbon emission spillover effects, and explored their influencing factors using the quadratic assignment procedure method. The results are as follows. (1) The carbon emission correlations in the Yangtze River Delta, Pearl River Delta, and Chengdu-Chongqing urban agglomerations increased, while Beijing-Tianjin-Hebei, Central Plains, and Middle Reaches of Yangtze River urban agglomerations decreased. (2) From a macro perspective, the Yangtze River Delta and Pearl River Delta urban agglomerations were at the core of the network, and the “bridging” role of the Middle Reaches of Yangtze River urban agglomeration was evident. From a micro perspective, the cities in the middle of urban agglomerations were more likely to become network cores. (3) Spillover effects between plates were present in the correlation network, with a gradual balance between the reception and spillover relationships. Most urban agglomerations exhibited an uneven distribution of community correlation capacity. (4) Geographic adjacency and urbanization level significantly promoted carbon emission correlation, although the direction and magnitude of other factors’ influence varied. The findings contribute to the implementation of low-carbon planning, carbon reduction strategies and regional sustainable development.

A system dynamics approach for sustainable groundwater management considering climate change, inter-organizational relationships, and domestic water saving

This article uses system dynamic modeling to examine the relationship between water allocation from the Ilam dam and groundwater fluctuation, as well as the effects of climate change, changes in inter-organizational relationships, and water demand management on water allocation and groundwater level fluctuations. The results showed that agricultural water allocation has a direct effect on groundwater level fluctuations. In the first scenario, which examined the effects of climate change, the comparison of three climate change scenarios (SSP126, SSP245, and SSP585) was carried out. The results illustrated that with temperature increasing and precipitation decreasing occurred in SSP585, the groundwater drawdown increases about 0.8 m more than SSP126. The second scenario examined the effects of changes in inter-organizational relationships. Based on the results of centrality measures in the social network analysis method, it was found that the organizational power of the stakeholder organization in the agriculture sector is greater than the ones in the environment sector. This causes the allocation to the agriculture sector to be prioritized over the environment. This scenario was analyzed by assuming a change in organizational power and giving priority to the water allocation of the environmental sector. The results showed that environmental needs (maximum 0.3 MCM) were almost entirely met in the second scenario, compared to the basic scenario. Also, the decrease in agricultural water allocation has led to an increase in groundwater usage by farmers, resulting in a 20 cm drop in groundwater levels. Therefore, regarding the sustainable management of water resources, directing attention towards alternative livelihoods for farmers can contribute to environmental water rights and the remediation of aquifers. The third scenario examined the effects of water demand management. The results showed that saving 20–25% of water in the domestic sector can reduce 40–50 cm maximum groundwater drawdown which align with sustainable groundwater management.

Labor Mobility Networks and Green Total Factor Productivity

Population migration continues to reshape the spatial pattern of China’s population and regional economic development. During this internal migration process, production and consumption patterns often change, ultimately leading to changes in green total factor productivity. This paper, based on the Chinese population census data and 1% sampling survey data from 2005 to 2015, utilizes social network analysis methods to measure the labor mobility network indicators of 284 prefecture-level cities. Further, this paper analyzes the impact and mechanisms of regional network status on green total factor productivity using a panel fixed effects model. We find that as network density increases, the interpersonal connections between regions become closer, and the network exhibits a clear pattern of “concentrated inflows” and “dispersed outflows”, with the trend of forming strong alliances becoming increasingly apparent. Regions positioned centrally either in terms of network in-degree or out-degree exhibit higher green total factor productivity. Among these, the labor mobility network plays a crucial role in enhancing green total factor productivity through the channel of technology diffusion effects, which improve investment efficiency via knowledge exchange and material capital accumulation. The promotive effect of labor network status on green total factor productivity is more pronounced in the eastern regions, where talent quality is higher, and in areas with fewer restrictions from the household registration system.

Examining Teachers' Professional Relationships, Friendship, and Trust Networks via Social Network Analysis

In this study, Social Network Analysis method was used to determine the professional relationships, friendships and trust networks that teachers established with their colleagues. The study group of the research consists of 35 teachers working in a public secondary education institution in Aydın province. A three-question structured interview form developed by the researchers was used to determine teachers' social networks. Data analysis was carried out through the UCINET 6.0 program. When the findings regarding the positions of teachers in the professional relationship network, friendship network and trust network were examined, it was determined that the network structure with the highest number of connections was the friendship network (ties = 97), followed by the professional relationship (ties = 95) and the trust network (ties = 66). In all social networks, teachers with mathematics, guidance, literature and biology branches were found to be at the center of the network, acting as a bridge between other actors and providing information and resource flow. According to the findings, it was determined that the network with the highest density (dens. = 0.082), bilateral (dia. = 0.198) and triad (triad = 0.330) reciprocity rate was the friendship network. This study, which examines the professional relationships, friendships and trust networks that teachers establish with their colleagues, is thought to be important in revealing the relationships between teachers and analyzing the current situation.

Faculty members as academic knowledge brokers in Iran's health sector: a social network analysis study

BackgroundInteraction between researchers and policymakers is an essential factor to facilitate the evidence-informed policymaking. One of the effective ways to establish this relationship and promote evidence-informed policymaking is to employ people or organizations that can play the role of knowledge brokers. This study aims to analyze the communication network and interactions between researchers and policymakers in Iran's health sector and identify key people serving as academic knowledge brokers.MethodsThis study was a survey research. Using a census approach, we administered a sociometric survey to faculty members in the health field in top ten Iranian medical universities to construct academic-policymaker network using social network analysis method. Network maps were generated using UCINET and NetDraw software. We used Indegree Centrality, Outdegree Centrality, and Betweenness Centrality indicators to determine knowledge brokers in the network.ResultsThe drawn network had a total of 188 nodes consisting of 94 university faculty members and 94 policymakers at three national, provincial, and university levels. The network comprised a total of 177 links, with 125 connecting to policymakers and 52 to peers. Of 56 faculty members, we identified four knowledge brokers. Six policymakers were identified as key policymakers in the network, too.ConclusionsIt seems that the flow of knowledge produced by research in the health field in Iran is not accomplished well from the producers of research evidence to the users of knowledge. Therefore, it seems necessary to consider incentive and support mechanisms to strengthen the interaction between researchers and policymakers in Iran's health sector.

FairSNA: Algorithmic Fairness in Social Network Analysis

In recent years, designing fairness-aware methods has received much attention in various domains, including machine learning, natural language processing, and information retrieval. However, in social network analysis (SNA), designing fairness-aware methods for various research problems by considering structural bias and inequalities of large-scale social networks has not received much attention. In this work, we highlight how the structural bias of social networks impacts the fairness of different SNA methods. We further discuss fairness aspects that should be considered while proposing network structure-based solutions for different SNA problems, such as link prediction, influence maximization, centrality ranking, and community detection. This survey-cum-vision clearly highlights that very few works have considered fairness and bias while proposing solutions; even these works are mainly focused on some research topics, such as link prediction, influence maximization, and PageRank. However, fairness has not yet been addressed for other research topics, such as influence blocking and community detection. We review the state of the art for different research topics in SNA, including the considered fairness constraints, their limitations, and our vision. This survey also covers evaluation metrics, available datasets and synthetic network generating models used in such studies. Finally, we highlight various open research directions that require researchers’ attention to bridge the gap between fairness and SNA.

Youth political participation and digital movement in Indonesia: the case of #ReformasiDikorupsi and #TolakOmnibusLaw

The younger generations have always been at the forefront of and contributed significantly to social movements that are critical of state policies or respond to socio-political situations. This is also what happened in large movements involving young people in Indonesia, conventionally in the form of demonstrations and digital movements. This study seeks to explore the relationship between youth political participation, digital media, and the development of youth political identity by analyzing young people’s engagement in some of the most significant political protests in Indonesia during the mass protests of #ReformasiDikorupsi, a movement opposing the amendments of several important bills, and #TolakOmnibusLaw, a movement opposing the establishment of a new single bill related to labor and investment, between 2019 and 2020. By analyzing quantitative data through data mining from Twitter from October 2019 to November 2020 with the social network analysis method, this study discusses online youth participation in social movements and the way they shape their political identity. Our research found that young people play an active and central role in building online and offline movements. In particular, the online movement contributes to the growth of the offline protest movement, and vice versa, because of concerns about the practice of illiberal democracy.

Detecting Upcoming Patent Keywords by Predicting Keyword Trends Using Patent Keyword Network

In this ever-changing technological landscape, the ability to quickly predict technological trends becomes crucial for any company or institute engaged in informed decision-making and strategic planning. Data for predicting technological trends can come from various sources such as patent data, which is easily accessible to the public due to the nature of patents. This research is aimed at patent analysis, focusing on combining the keyword- based method, social network analysis (SNA) method, and neural network prediction to propose a feasible keyword trend prediction method based on patent analysis by targeting upcoming keyword trends. More specifically, we utilize Long Short-Term Memory (LSTM) to predict changes in keyword frequency using keyword centralities as input. To assess the effectiveness of the proposed method, we constructed the input dataset using the USPTO patent database in the Information and Communication Technology (ICT) field. We then experimented to compare the proposed method with the benchmark method. Furthermore, to counteract the unbalanced nature of patent data, the SMOGN method is introduced. The results demonstrate its potential for application in broader contexts.

A Team Cognition Measurement Method for Single Pilot Operations Human-Machine System Design

Team cognition is an important indicator to reflect the trend of single pilot operation (SPO) human-machine system team performance. How to measure team cognition to explore different function allocation decisions before the implementation of prototypes or simulators has become a significant step in the early SPO human-machine system design. Therefore, this article proposed a team cognition measurement method based on the social network analysis (SNA) method, namely cognitive social network analysis (CSNA). The CSNA method firstly identified the nodes, edge directions and channel resource values of each task; secondly calculated the cognitive demands of each task based on multiple resource theory; thirdly summed the cognitive demands of the same task to construct the association matrix; and finally calculated global and nodal network metrics. The results of the study showed that (1) the CSNA method could identify design flaws of the initial SPO human-machine system; (2) the CSNA method increased the ability to identify the changes in both the cognitive demands of tasks and the overall workloads of human agents compared to the SNA method; (3) the CSNA method could help researchers make reasonable design suggestions to improve the team performance of the redesigned SPO human-machine system. The above results not only verify the feasibility of the CSNA method, but also show that the CSNA method is superior to the current SNA method.

What topics of peer interactions correlate with student performance in physics courses?

Research suggests that interacting with more peers about physics course material is correlated with higher student performance. Some studies, however, have demonstrated that different topics of peer interactions may correlate with their performance in different ways, or possibly not at all. In this study, we probe both the peers with whom students interact about their physics course and the particular aspects of the course material about which they interacted in six different introductory physics courses: four lecture courses and two lab courses. Drawing on social network analysis methods, we replicate prior work demonstrating that, on average, students who interact with more peers in their physics courses have higher final course grades. Expanding on this result, we find that students discuss a wide range of aspects of course material with their peers: concepts, small-group work, assessments, lecture, and homework. We observe that in the lecture courses, interacting with peers about concepts is most strongly correlated with final course grade, with smaller correlations also arising for small-group work and homework. In the lab courses, on the other hand, small-group work is the only interaction topic that significantly correlates with final course grade. We use these findings to discuss how course structures (e.g. grading schemes and weekly course schedules) may shape student interactions and add nuance to prior work by identifying how specific types of student interactions are associated (or not) with performance.

Concerning the Defamation and Execution of the “Radical” Ludwig Hätzer (1500–1529): An Attempt at Using Social Network Analysis on Small Samples

Abstract This paper uses the method of Social Network Analysis to generate new insights into the life and trial of the dissident theologian and translator Ludwig Hätzer. With the help of the programme visone, the corpus of letters in which Hätzer is mentioned during his lifetime is visualised and analysed, incorporating the different evaluations Hätzer receives in the correspondence. The network analysis reveals the important role the brothers Ambrosius and Thomas Blarer in Constance played in connecting different parts of the network. It also shows that communication between theologians and cities seems to have taken place conspicuously separately. The paper argues that letters by individuals like Wolfgang Capito and Martin Bucer might, in the end, have been more relevant in preparing the ground for Hätzer’s execution than polemical pamphlets. It also reveals that the turning point for Hätzer’s reputation must have been his acquaintance with Hans Denck.

Youth political participation and digital movement in Indonesia: the case of #ReformasiDikorupsi and #TolakOmnibusLaw

The younger generations have always been at the forefront of and contributed significantly to social movements that are critical of state policies or respond to socio-political situations. This is also what happened in large movements involving young people in Indonesia, conventionally in the form of demonstrations and digital movements. This study seeks to explore the relationship between youth political participation, digital media, and the development of youth political identity by analyzing young people’s engagement in some of the most significant political protests in Indonesia during the mass protests of #ReformasiDikorupsi, a movement opposing the amendments of several important bills, and #TolakOmnibusLaw, a movement opposing the establishment of a new single bill related to labor and investment, between 2019 and 2020. By analyzing quantitative data through data mining from Twitter from October 2019 to November 2020 with the social network analysis method, this study discusses online youth participation in social movements and the way they shape their political identity. Our research found that young people play an active and central role in building online and offline movements. In particular, the online movement contributes to the growth of the offline protest movement, and vice versa, because of concerns about the practice of illiberal democracy.

Analysis of the Spatial Correlation Network and Driving Mechanism of China’s Transportation Carbon Emission Intensity

From 2008 to 2021, this study analyzed the spatial correlation characteristics between provincial transportation carbon emission intensity and explored ways to reduce transportation carbon emissions. This study used the modified gravity model, social network analysis (SNA) method, and temporal exponential random graph model (TERGM) to analyze the spatial correlation network evolution characteristics and driving mechanism of China’s transportation carbon emission intensity. This study found that China’s transportation carbon emission intensity and spatial correlation network have unbalanced characteristics. The spatial correlation network of transportation carbon emission intensity revealed that Shanghai, Beijing, Tianjin, Guangdong, Fujian, and other provinces were at the center of the network, with significant intermediary effects. The spatial correlation of transportation carbon emission intensity was divided into four functional plates: “two-way spillover”, “net benefit”, “broker”, and “net spillover”. The “net benefit” plate was mainly located in developed regions, and the “net spillover” plate was primarily located in underdeveloped regions. Endogenous structural and exogenous mechanism variables were the main factors affecting the evolution of the spatial correlation network of provincial transportation carbon emission intensity.

Construction and evolutionary factors of spatial correlation network of China's provincial tourism resource conversion efficiency

Research objectiveTo explore the spatial correlation network characteristics and formation mechanisms of tourism resource conversion efficiency, and provide reference for the collaborative improvement of tourism resource conversion efficiency at the provincial level in China. Research methodsNon parametric SBM efficiency measurement method and social network analysis method. Research hypothesis: The spatial network correlation characteristics of tourism resource conversion efficiency are obvious, and regional connections are close. Research findings(i) during the research period, the spatial connection strength of China's tourism resource conversion efficiency continued to increase and the spatial network structure of tourism resource conversion efficiency tended to become more complex and significantly more stable. (ii) A spatially linked network with a stable tourism resource conversion efficiency structure formed in China. The number of network relations and density of the network fluctuated and increased, while the network efficiency continued to decrease; however, a strong small-world nature was observed. (iii) The economic development level difference matrix, tourism industry agglomeration difference matrix, human capital difference matrix, and marketization degree difference matrix significantly and positively affected spatial association relationship establishment, while the provincial adjacency matrix significantly and negatively affected such relationships.

A bibliometric analysis of the evolving mechanisms of shadow education research

AbstractShadow education has burgeoned into a global phenomenon, and over the last 30 years, a substantial body of literature has emerged to explore this trend. Drawing on bibliometric data from 377 articles retrieved from the Web of Science (WOS), this study investigates the evolving landscape of shadow education research. It examines historical publication trends through performance analysis, intellectual sources and evolving themes using co‐citation analysis, complemented by co‐word analysis. Major authors and collaboration dynamics are discerned through co‐authorship analysis, contributing to a comprehensive understanding of collaborative networks. Additionally, the study constructs an evolution path for shadow education research published in journals, employing citation analysis supplemented by social network analysis methods. In conclusion, the study not only illuminates the historical trajectory of shadow education as a concept but also poses pertinent questions and offers recommendations for future research. Our research significantly contributes to the comprehension of how shadow education has evolved from a nascent concept into a global research discourse.

Leveraging AI and Blockchain for Privacy Preservation and Security in Fog Computing

INTRODUCTION: Cloud computing's offshoot, fog computing, moves crucial data storage, processing, and networking capabilities closer to the people who need them. There are certain advantages, such improved efficiency and lower latency, but there are also some major privacy and security concerns. For these reasons, this article presents a new paradigm for fog computing that makes use of blockchain and Artificial Intelligence (AI).
 OBJECTIVES: The main goal of this research is to create and assess a thorough framework for fog computing that incorporates AI and blockchain technology. With an emphasis on protecting the privacy and integrity of data transactions and streamlining the management of massive amounts of data, this project seeks to improve the security and privacy of Industrial Internet of Things (IIoT) systems that are cloud-based.
 METHODS: Social network analysis methods are utilised in this study. The efficiency and accuracy of data processing in fog computing are guaranteed by the application of artificial intelligence, most especially Support Vector Machine (SVM), due to its resilience in classification and regression tasks. The network's security and reliability are enhanced by incorporating blockchain technology, which creates a decentralised system that is tamper resistant. To make users' data more private, zero-knowledge proof techniques are used to confirm ownership of data without actually disclosing it.
 RESULTS: When applied to fog computing data, the suggested approach achieves a remarkable classification accuracy of 99.8 percent. While the consensus decision-making process of the blockchain guarantees trustworthy and secure operations, the support vector machine (SVM) efficiently handles massive data analyses. Even in delicate situations, the zero-knowledge proof techniques manage to keep data private. When these technologies are integrated into the fog computing ecosystem, the chances of data breaches and illegal access are greatly reduced.
 CONCLUSION: Fog computing, which combines AI with blockchain, offers a powerful answer to the privacy and security issues with cloud centric IIoT systems. Combining SVM with AI makes data processing more efficient, while blockchain's decentralised and immutable properties make it a strong security measure. Additional security for user privacy is provided via zero-knowledge proofs. Improving the privacy and security of fog computing networks has never been easier than with this novel method.

Based on social network analysis: Whether international trade network changed before and after the outbreak of the COVID-19 pandemic?

Since the outbreak of COVID-19 pandemic, the international trade has been facing a severe impact. Based on the social network analysis method, this research models the world trade network during the six years from 2017 to 2022. The world trade network is made into a weighted directed adjacency matrix based on the data published by UN Comtrade database. The matrix also refers to the gravity model. With visualized data and images, this research makes a quantitative study on the impact of the COVID-19 pandemic on the world trade network from the perspective of network analysis, centrality analysis and clustering coefficient. Apart from that, this research focuses on the international trade pattern and how it changes under the impact of the pandemic. According to this research, although the world trade network has not been deeply affected by the disease, there are some concealed tendency has been discovered, which may profoundly change the world pattern in the future.

The battle of hashtag on Twitter: Unraveling the pioneers of influence in social media marketing

<p><em>This research aims to identify Key Opinion Leaders (KOLs) in hashtag marketing campaigns on social media using Social Network Analysis (SNA) method. The study explores the social network formed in the campaign between #TimPakeSendok and #TimPakeTangan to identify influential users who play a significant role in the dissemination and reach of the campaign based on degree centrality, betweenness centrality, and eigenvector centrality analysis. The study finds that actors with many followers on Twitter tend to have high centrality levels, both in terms of degree centrality, betweenness centrality, and eigenvector centrality, indicating that they are actively connected to other actors in the network, act as effective intermediaries between other actors, and have meaningful connections with other influential actors in the network during this hashtag marketing campaign. However, the closeness centrality measurement indicates that actors with significant closeness centrality are those with fewer followers. Other findings show that sharing opinions and interacting with other users in the hashtag campaign create a sense of togetherness and affinity within groups with similar interests.</em></p>

Research on Chengdu-Chongqing Urban Agglomeration and Guangdong-Hong Kong-Macao Greater Bay Area Innovation Network Structure

Under the background of factor-driven to innovation-driven, urban innovation is very important in the regional development pattern.In this paper, Chengdu-Chongqing urban agglomeration and Guangdong-Hong Kong-Macao Greater Bay Area are taken as the research objects. Based on the co-authored data of papers among cities, a theoretical knowledge innovation network is constructed.With the help of social network analysis method, the overall characteristics and network centrality are compared and analyzed, thus revealing the development status of Chengdu-Chongqing urban agglomeration and Guangdong-Hong Kong-Macao Greater Bay Area knowledge innovation network.The empirical research shows that: (1) During the research period, the overall density and agglomeration capacity of Guangdong-Hong Kong-Macao Greater Bay Area theoretical knowledge innovation cooperation network are higher than those of Chengdu-Chongqing urban agglomeration. (2) Dual-core cities Chongqing, Chengdu, Guangzhou and Shenzhen have significant innovation polarization effects.