Personal vs. know-how contacts: which matter more in wiki elections?

오피니언 마이닝과 네트워크 분석을 활용한 상품 커뮤니티 분석: 영화 흥행성과 예측 사례

A social network is a set of relationships and interactions among social entities such as individuals, organizations, and groups. The social network analysis is one of the major topics in the ongoing research field. The major problem regarding the social network is finding the most influential objects or persons. Identification of most influential nodes in a social network is a tedious task as large numbers of new users join the network every day. The most commonly used method is to consider the social network as a graph and find the most influential nodes by analyzing it. The degree centrality method is node based and has the advantage of easy identification of most influential nodes. In this paper a method called "Enhanced Degree Centrality Measure" is proposed which integrates clustering co-efficient value along with node based degree centrality. The enhanced degree centrality measure is applied to three different datasets which are obtained from the Facebook to analyze the performance. The response obtained is compared with existing methods such as degree centrality method and SPIN algorithm. By comparison, it is found that highest number of active nodes identified by the proposed method is 64 when compared with SPIN algorithm which identifies only 55.

ABSTRACTPurposeThis study examined how social network strength (i.e., the intensity and frequency of interactions within one's personal network, assessed using centrality measures such as degree, closeness, betweenness, and eigenvector centrality) affects mental health.MethodsThe study was conducted with 108 first‐year university students. Data were collected using a structured questionnaire, and NetMiner 4.0 was employed to analyze degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality as indicators of social network centrality. Additionally, Quadratic Assignment Procedure (QAP) correlation analysis and QAP regression analysis were conducted to assess the relationship between social network strength and mental health.ResultsThe study found that stronger social networks were linked to better mental health in late adolescence. Specifically, higher social network centrality was associated with lower levels of hopelessness and depression. QAP correlation analysis revealed that hopelessness had a significant negative correlation with in‐closeness centrality (which reflects how easily one can be reached by others within the network) (r = −0.252, p < 0.05), while depression was negatively correlated with in‐degree centrality (the number of direct incoming connections from peers) (r = −0.233, p < 0.05), in‐closeness centrality (r = −0.256, p < 0.05), and eigenvector centrality (a measure of how well a person is connected to popular or influential members of the network) (r = −0.291, p < 0.01). QAP regression analysis further confirmed that weaker social ties, indicated by higher out‐closeness centrality (how easily one can reach others, especially those at a distance), were associated with higher depressive symptoms (adj. R2 = 0.143, F = 2.423, p < 0.05). These results suggest that building stronger and more integrated social networks may help reduce psychological distress and support mental well‐being in late adolescence.ConclusionStronger social networks in late adolescence are associated with better mental health, highlighting the importance of social connections. While correlational findings suggest general links between centrality and emotional well‐being, the regression results underscore specific predictors with practical implications. In‐degree, in‐closeness, and eigenvector centrality were associated with reduced depressive symptoms, indicating that being well‐integrated within a social network may offer protective benefits. In contrast, high out‐closeness centrality predicted increased depression, suggesting that the effort required to maintain outward‐reaching connections may impose psychological burdens. These findings suggest that targeted interventions should focus on strengthening inward social integration while addressing the stress related to maintaining extensive outward connections, providing direction for more effective youth mental health strategies.

This study aimed to produce a network structure in Integrated Urban Farming Program in Bandung City to map the involved policy actors to realize a food-smart city. In this study, a mixed method was used with an exploratory sequential strategy involving policy actors from the government, private sector, academia, community, and mass media. To obtain a network structure in Integrated Urban Farming toward determining the most important actors, the Social Network Analysis (SNA) approach was also employed through the Gephi application. From this context, the structure emphasized four dimensions, namely Degree, Betweenness, Closeness, and Eigenvector Centralities. The results showed that the actor with the most connections (degree of centrality) and best communication control (betweenness centrality) is Parahyangan Catholic University (academic). At the same time, the actor that plays the most important role (eigenvector) is at the lower level of the government’s Sub-District and Urban Village. This study is useful for explaining the importance of the position of actors in the urban farming policy network, which is the key to the success of a program.

코로나(Covid19) 사태로 인해 자동차 공급사슬에서의 단절(Disruption)로 인한 문제가 더욱 부각 되었고 무엇보다 안정적인 공급체인 구성이 기업의 생존에 화두가 되고 있다. 특히 복잡한 공급사슬 구조를 가져 유기적으로 연결되어 있는 자동차 부품산업은 각 공급업체별 개별적 분석으로 전체를 이해하는데 한계점이 존재한다. 따라서 좀 더 확장된 시각에서의 폭넓은 부품 네트워크의 실증적 분석에 대한 요구가 높아지고 있다. 본 연구에서는 한국자동차산업을 사회연결망분석(SNA_Social Network Analysis)기반하여 부품 네트워크를 분석하였다. 기존 연구에서는 각 개별 기업별 중심성(연결 중심성, 매개 중심성, 근접 중심성)을 특정 부품(브레이크 등)으로 국한하여 연구가 진행되었다. 본 연구에서는 자동차에 포함되는 부품들을 기능별로 구분하여 대부분의 주요 부품을 포함시켜서 각 부품의 한국자동차산업 네트워크의 특성을 분석하였다. 한국자동차사산업의 부품기능별로 체계적인 분석을 위하여 산업현장에서 통용되고 있는 분류체계 중에서 대한무역투자진흥공사(KOTRA)에서 수출입 자동차 부품 집계용으로 분류 사용하고 있는 6대 분류체계를 본 연구에 적용하였다. 이에 따른 연구 결과로 외향연결중심성(방향성 있음/없음)과 매개 중심성(방향성 없음)에서 부품기능별 유의미한 차이점을 확인할 수 있었다. 이를 통해 한국자동차산업에서의 부품기능별 부품 네트워크 특성 이해와 더불어 한국자동차산업 정책 전반에 참조할 수 있는 시사점을 제공하고자 하는데 본 연구의 목적이 있다.Understanding the automotive supply chain structure has received greater focus due to the disruption caused by Covid-19. Above all, a sustainable supply chain is now essential for companies’ survival. Particularly, the automobile parts industry, which has a complex supply chain structure, is difficult to understand through individual supplier analysis. Therefore, to provide better understanding for the structure of the auto parts supply chain, it is necessary to analyze the broad network. We analyzed the auto parts network of the Korean automobile industry using social network analysis (SNA). Previous studies considered the centrality of each individual company (degree centrality, betweenness centrality, closeness centrality) with specific parts (brakes, etc.). This study included the auto parts, classified by function and most major parts, and analyzed the characteristics of the network of the Korean automobile industry by part We found significant differences among part groups based on function with degree centrality and betweenness centrality. This study can provide the automobile industry policy suggestions through understanding of the characteristics of the Korean automobile parts network.

In 2020, the world is facing a Covid-19 virus pandemic. The fields of epidemiology and networks are needed in dealing with its spread. Individual (contact) tracing is an important control measure in the spread of infectious diseases. The network of contacts describes the potential pathways for the spread of the disease. Social Network Analysis (SNA) is the study of structural construction based on graph theory with Degree Centrality, Betweenness Centrality, and Closeness Centrality. In this paper, the SNA graph provides a description of the contact network in a resident cluster. Visualization of the formed SNA graph provides information on the pattern of contact relationships in the cluster. The measurement of centrality in the SNA method identifies who has a high value of connectedness and closeness in the cluster. The calculation of degree centrality shows that node-F391 is the node with the highest interaction, which is 3,203. This indicates that node interacts the most with residents in its environment within 14 days before and after being positive for COVID-19 based on the date of the swab test. The calculation of Betweenness Centrality shows that nodes-B041 and C371 are important actors with the highest value of 0.006. The two node becomes an important link. The use of graphs on SNA provides visualization of the contact network to distinguish potential relationships between close contacts, so that contact tracing can be carried out based on disease path mapping. Compared to the conventional way of reviewing individual contact records, graph visualization on the SNA is a good adjunct tool for healthcare workers in contact tracing investigations.

The twitter social network is widely used to discuss all kinds of topics, including those related to politics. Analyzing online conversations on Twitter to map the popularity of political figures as candidates for the Indonesian presidential election is a popular and challenging research area. In the Twitter network, citizens can express themselves and communicate with political figures. The conversational data in Twitter is very complex, so Business Intelligence is needed to transform raw data into meaningful and useful information to see the popularity of Indonesian presidential election candidates. The analysis used is Social Network Analysis (SNA) by measuring Degree Centrality, Eigenvector Centrality, Betweenness Centrality, Closeness Centrality. The presidential candidates in this study, Ganjar Pranowo with a twitter account “ganjarpranowo”, Puan Maharani with a twitter account “puanmaharani_ri”, and Anies Baswedan with a twitter account “aniesbaswedan”. The actor "aniesbaswedan" excels in the value of degree centrality and betweenness centrality. The “aniesbaswedan” account is the actor who has the most influence on social network interactions based on the total number of interactions generated, then the account also becomes a bridge or liaison in the interactions of other actors in the network.

Co-authorship networks and research impact: A social capital perspective

Centrality, which quantifies the importance of individual nodes, is among the most essential concepts in modern network theory. As there are many ways in which a node can be important, many different centrality measures are in use. Here, we concentrate on versions of the common betweenness and closeness centralities. The former measures the fraction of paths between pairs of nodes that go through a given node, while the latter measures an average inverse distance between a particular node and all other nodes. Both centralities only consider shortest paths (i.e., geodesics) between pairs of nodes. Here we develop a method, based on absorbing Markov chains, that enables us to continuously interpolate both of these centrality measures away from the geodesic limit and toward a limit where no restriction is placed on the length of the paths the walkers can explore. At this second limit, the interpolated betweenness and closeness centralities reduce, respectively, to the well-known current-betweenness and resistance-closeness (information) centralities. The method is tested numerically on four real networks, revealing complex changes in node centrality rankings with respect to the value of the interpolation parameter. Nonmonotonic betweenness behaviors are found to characterize nodes that lie close to intercommunity boundaries in the studied networks.

Food addiction (FA) is a condition in which ultra-processed foods (UPFs) activate the brain's reward pathways, leading to binge eating, loss of control, and continued consumption despite negative consequences. It can appear early in childhood and is linked to behavioral, sociodemographic, and metabolic factors. This study assessed the contribution of FA, its structure, and connectivity in relation to sociodemographic, nutritional status, and metabolic variables in network analysis. A cross-sectional study was conducted with 93 children (7-11years old) living in Vitória de Santo Antão, Brazil. FA was assessed using the Yale Food Addiction Scale for Children, which was translated and validated for the Brazilian child population. Sociodemographic (age, sex, race, socioeconomic class), anthropometric (body weight, height, waist circumference, BMI, BMI-for-age, body fat percentage, lean mass, and fat mass), and metabolic (blood pressure, total cholesterol, triglycerides, HDL, LDL, and fasting glucose) factors were analyzed. For network analysis, the degree centrality (DC), closeness centrality (CC), betweenness centrality (BC), and eigenvector centrality (EC) were evaluated. FA exhibited moderate centrality in sociodemographic and metabolic networks, acting as a connector between key variables such as age and socioeconomic class (BC = 0.071-0.500; EC = 0.301-0.500; CC = 0.636-0.667). These metrics indicate that FA, while not dominant, maintains access to influential nodes and participates in relevant information pathways. In contrast, within the anthropometric network, FA showed a peripheral role, with fewer direct links (DC = 0.222-0.285) and limited intermediation (BC = 0.111). Variation in centrality across domains underscores the selective integration of FA, suggesting that its impact is context dependent.

Preventing power transmission failures in its network of electrical terminal stations is a major concern for the infrastructure of the Thai capital Bangkok and its vicinity. Towards this objective the present study aims to analyse the network and its reliability under conditions of increased demand. The analysis is based on a representation of the network as a graph allowing to identify the most important terminal stations by graph-theoretical terms. These are, in particular, the centrality measures Degree Centrality (DC) giving the number of a station’s one-hop neighbours, Closeness Centrality (CC) describing the efficiency of power transmission from one station to others, shortest-path Betweenness Centrality (BC) indicating the number of a station’s occurrences on the shortest paths between indirectly connected stations, Hub describing stations that are connected to a large number of important stations, and Authority indicating the stations that connect many important stations. Experimental results revealed that the Bangkok Noi station was most significant when the measures DC, CC and BC were considered and, on the other hand, that the North Bangkok station was vital in terms of CC, Hub and Authority. Therefore, these stations need to be closely monitored and their operation to be carried out with extreme care in order to prevent the occurrence of power transmission failures within the Bangkok metropolitan area.

Calculating centrality measures has been used to identify Cyber-physical System critical component for some time. However, accurate identification of critical elements within the interconnected system that are vulnerable to system failures and intentional attacks requires not only a reliable model but robust index to dynamic system operating environment. These measures include 1) Node Degree, 2) Node Importance, 3) Betweenness Centrality, 4) Closeness Centrality and 5) Eigenvector Centrality. System component criticalities have been evaluated based on these measures in this paper and their performances and rankings under different network topologies and operating conditions have been compared using 1) Pearson Correlation Coefficient and 2) Spearman Correlation Coefficient. This has enabled the quantification of the optimality of using different centrality measures in different network scenarios. The results suggest that Betweenness and Degree centralities are the most suitable indices for identifying critical electrical power system bus and cyber system node individually. Moreover, the degree of correlation between directed measures has suggested also the necessity of using directional model in Cyber-physical System studies.

We present correlation analysis between the centrality values observed for nodes (a computationally lightweight metric) and the maximal clique size (a computationally hard metric) that each node is part of in complex real-world network graphs. We consider the four common centrality metrics: degree centrality (DegC), eigenvector centrality (EVC), closeness centrality (ClC) and betweenness centrality (BWC). We define the maximal clique size for a node as the size of the largest clique (in terms of the number of constituent nodes) the node is part of. The real-world network graphs studied range from regular random network graphs to scale-free network graphs. We observe that the correlation between the centrality value and the maximal clique size for a node increases with increase in the spectral radius ratio for node degree, which is a measure of the variation of the node degree in the network. We observe the degree-based centrality metrics (DegC and EVC) to be relatively better correlated with the maximal clique size compared to the shortest path-based centrality metrics (ClC and BWC).

Information and communication technology (ICT) outsourcing to suppliers has the potential to enhance buyers’ product innovation and overall performance. However, the impact of technology outsourcing to lower-tier suppliers on buyers’ financial performance in multi-tier supply chain networks remains unclear. Unlike prior dyadic studies that focus on a buyer-tier1 supplier perspective, our study extends the analytical scope to encompass buyer, tier-1, and tier-2 ICT supplier triads using multi-tier supply network data from Capital IQ. Building on existing literature indicating that a buyer’s betweenness centrality negatively affects its financial performance, we further find that tier-1 suppliers’ betweenness centrality also exhibits a negative relationship with buyers’ financial performance. In addition, we provide empirical evidence that a buyer’s timely supplier payment amplifies the positive effect of buyer degree centrality on its financial performance, while alleviating the positive effect of tier-1 suppliers’ degree centrality on buyers’ financial performance. Interestingly, timely supplier payment negatively moderates the association between a buyer’s closeness centrality and its financial performance, but positively moderates the corresponding association between tier-1 supplier closeness centrality and buyer financial performance. Our empirical analysis advances theoretical understanding of lower-tier suppliers and sheds light on the bridging role of tier-1 suppliers in the literature of multi-tier supply chain relationships. These findings highlight the benefits of technology-enabled multi-tier supply network structures and their interactions with buyer-tier1 supplier relationships. This study also provides practical insights for firms to improve financial outcomes through strategic ICT outsourcing to multi-layer suppliers.

The identification of influential nodes in complex networks is one of the most exciting topics in network science. The latest work successfully compares each node using local connectivity and weak tie theory from a new perspective. We study the structural properties of networks in depth and extend this successful node evaluation from single-scale to multi-scale. In particular, one novel position parameter based on node transmission efficiency is proposed, which mainly depends on the shortest distances from target nodes to high-degree nodes. In this regard, the novel multi-scale information importance (MSII) method is proposed to better identify the crucial nodes by combining the network’s local connectivity and global position information. In simulation comparisons, five state-of-the-art algorithms, i.e. the neighbor nodes degree algorithm (NND), betweenness centrality, closeness centrality, Katz centrality and the k-shell decomposition method, are selected to compare with our MSII. The results demonstrate that our method obtains superior performance in terms of robustness and spreading propagation for both real-world and artificial networks.