Social Network Centrality Research Articles

Social Media Networks and Stock Price Synchronicity: Evidence from a Chinese Stock Forum

The widespread communication and interaction between small investors on social media in the capital market has resulted in the formation of a social media network specific to listed companies. Using a unique dataset from an active online stock forum in China, we construct a social media network of listed companies by assigning listed companies as nodes and common commenters who post in each of the subforums for two listed companies in a year as edges. Then, we investigate the relationship between the network centrality of the listed companies and stock price synchronicity. The results show that there is a significantly negative relationship between the social network centrality of listed companies and stock price synchronicity, indicating that social media networks can improve the price efficiency of capital markets. The mechanism analysis shows that social media networks can produce more corporate‐specific information and improve the efficiency of information dissemination, thus reducing stock price synchronicity.

Beyond school: The role of technology in K-12 students' lives and informal learning

The lives of children and teenagers are saturated with digital environments with multi-purpose uses and satisfy various needs. This study aims to expose the significance they attribute to their activities, to find out how these activities meet their needs, and to discuss their relevance to the field of child-computer interaction. The study is based on a qualitative approach, raising the voices of 29 K-12 students. The findings' analysis combined a bottom-up thematic analysis with a top-down analysis based on the Uses and Gratifications Theory. The bottom-up thematic analysis revealed three themes: the centrality of social networks, gaming culture, and new skills across digital platforms. The top-down analysis reveals a new range of present-day uses and gratifications: security needs lead to discretion and different usage modes. The research revealed that using digital environments entails informal learning, ample visual language use, acquisition of knowledge, and identity development.

Simulation of policies to promote pro‐environmental behavior in a dynamic learning model of social networks: Cases of Brazil, the United States, Japan, Germany, China, and India

AbstractPro‐environmental behavior does not seem to diffuse sufficiently in Brazil, China, Germany, Japan, India, or the United States. Based on the current distribution of carbon footprints and learning patterns (DeGroot learning, best practice, and free riding) in social networks in the six countries, what will happen to the diffusion of pro‐environmental behaviors in each country? What policy measures will be most effective? This study aims to simulate the diffusion of pro‐environmental behavior and to test the effectiveness of various policy measures in a dynamic social network model in the six countries. The distribution of pro‐environmental behaviors is generated by global gridded carbon footprint data. The distribution of learning patterns in social networks is estimated in each country, using data from the World Values Survey. This study considers three policy measures: increasing the number of people pursuing best pro‐environmental behavior practices (best practice promotion, [BPP]), decreasing the number of free riders (free‐riding reduction, [FRR]), and converting the learning patterns of people with high degrees of centrality into best practice (centrality‐based best practice promotion, [CBPP]). Interesting findings were threefold. First, there are large differences in the estimated degrees of individual pro‐environmental behavior and the proportions of learning patterns in the six countries. Second, FRR and BPP are effective in promoting pro‐environmental behavior, but the choice of FRR or BPP depends on the initial proportion of free riders. Third, CBPP is the most effective policy of the three in all countries. Centrality in social networks must be considered when implementing intervention.

CTO Network Centrality and Corporate Innovation

Social network centrality of chief technology officers (CTOs) affects corporate innovation. Firms with CTOs who are well-connected within the network of all business professionals invest more in research and development (R&D), receive more patents, attract more patent citations, and create more patent value. Besides the effect of the general business network centrality, higher professional network centrality of CTOs (within the network of all other CTOs) also fosters corporate innovation. In addition, firms innovate more and better if CTOs are hired by the incumbent CEOs. Ultimately, R&D investment of companies with high-centrality CTOs enhances firm value.

The adaptive value of density-dependent habitat specialization and social network centrality

Density dependence is a fundamental ecological process. In particular, animal habitat selection and social behavior often affect fitness in a density-dependent manner. The Ideal Free Distribution (IFD) and niche variation hypothesis (NVH) present distinct predictions associated with Optimal Foraging Theory about how the effect of habitat selection on fitness varies with population density. Using caribou (Rangifer tarandus) in Canada as a model system, we test competing hypotheses about how habitat specialization, social behavior, and annual reproductive success (co)vary across a population density gradient. Within a behavioral reaction norm framework, we estimate repeatability, behavioral plasticity, and covariance among social behavior and habitat selection to investigate the adaptive value of sociality and habitat selection. In support of NVH, but not the IFD, we find that at high density habitat specialists had higher annual reproductive success than generalists, but were also less social than generalists, suggesting the possibility that specialists were less social to avoid competition. Our study supports niche variation as a mechanism for density-dependent habitat specialization.

Social network centrality and the corporate environment: The case of sexual diversity policies

AbstractWe study the external influence of social capital, measured by Facebook's (now Meta) Social Connectedness Index, on a firm's decision to adopt policies that promote a more diverse corporate environment. Recent studies find corporate policies that embrace sexual diversity are beneficial to firms and their stakeholders, thereby contributing to their corporate social responsibility (CSR) and business sustainability. We find that firms with a high social network centrality are more likely to adopt policies and business strategies that support sexual diversity. Moreover, firms that adopt good CSR practices are more likely to implement more inclusive policies such as sexual diversity policies. This provides for a more comfortable work environment for the LGBT+ community. However, we also find that firms reduce their adoption of inclusive policies during times of economic uncertainty, supporting the pessimistic view that firms decrease window dressing activities during economic downturns when governance is tightened, and resources are scarce. We attribute this phenomenon to the exploitation of agency benefits by managers.

Social network centrality predicts dietary decisions in a wild bird population

How individuals balance costs and benefits of group living remains central to understanding sociality. In relation to diet, social foraging provides many advantages but also increases competition. Nevertheless, social individuals may offset increased competition by broadening their diet and consuming novel foods. Despite the expected relationships between social behavior and dietary decisions, how sociality shapes individuals' novel food consumption remains largely untested in natural populations. Here, we use wild great tits to experimentally test how sociality predicts dietary decisions. We show that individuals with more social connections have higher propensity to use novel foods compared to socially peripheral individuals, and this is unrelated to neophobia, observations, and demographic factors. These findings indicate sociable individuals may offset potential costs of competition by foraging more broadly. We discuss how social environments may drive behavioral change in natural populations, and the implications for the causes and consequences of social strategies and dietary decisions.

Exploiting the Social-Like Prior in Transformer for Visual Reasoning

Benefiting from instrumental global dependency modeling of self-attention (SA), transformer-based approaches have become the pivotal choices for numerous downstream visual reasoning tasks, such as visual question answering (VQA) and referring expression comprehension (REC). However, some studies have recently suggested that SA tends to suffer from rank collapse thereby inevitably leads to representation degradation as the transformer layer goes deeper. Inspired by social network theory, we attempt to make an analogy between social behavior and regional information interaction in SA, and harness two crucial notions of structural hole and degree centrality in social network to explore the possible optimization towards SA learning, which naturally deduces two plug-and-play social-like modules. Based on structural hole, the former module allows to make information interaction in SA more structured, which effectively avoids redundant information aggregation and global feature homogenization for better rank remedy, followed by latter module to comprehensively characterize and refine the representation discrimination via considering degree centrality of regions and transitivity of relations. Without bells and whistles, our model outperforms a bunch of baselines by a noticeable margin when considering our social-like prior on five benchmarks in VQA and REC tasks, and a series of explanatory results are showcased to sufficiently reveal the social-like behaviors in SA.

Characterizing social networks and influence of early-life social housing in weaned heifers on pasture

Dairy cattle are gregarious animals that are commonly managed in social groups, yet group-level social dynamics remain underexplored in weaned heifers. We characterized activity and social networks after weaned heifers had been raised in social groups on pasture for approximately 2 mo and examined effects of preweaning social housing. Holstein heifers raised in individual pens (n = 17) or paired pens (n = 20; 10 pens of 2) were mingled between treatments and grouped (10–11 heifers/group; total of 4 groups observed) on pasture following weaning (8.8 ± 0.4 wk of age; mean ± standard deviation). When heifers were 17.8 ± 1.0 (mean ± standard deviation) wk of age, we conducted live observation over a period of 5 d (6 h/observation day; morning: 0800 to 1100 h and afternoon: 1200 to 1500 h) for a total of 30 h observation/group. Using instantaneous scans at 10 min intervals, we recorded behavior (feeding, lying, or standing) and social proximity (<3 body lengths of another heifer, with neighbor identity noted) of all heifers. Duration of lying and feeding did not differ between previous housing treatments, but heifers reared in pairs stood for longer in the morning than previously individually housed heifers (30% vs. 24% of scans; standard error = 0.03). Networks of different behaviors showed limited correlation, with some variability between groups. Centrality in social networks was minimally affected by preweaning social housing, although previously pair-housed calves had greater strength (sum of an individual's edge weights) and eigenvector centrality (sum of the centralities of an individual's connections) in the lying social network for one group. Preweaning pair assignment was correlated with network structure for lying and standing networks for some groups. These results suggest that preweaning social housing may subtly affect activity and social behavior longer-term, but that behavior may be most subject to current social context.

Efficiency and Vulnerability in Networks: A Game Theoretical Approach

Defining measures of network efficiency and vulnerability is a pivotal aspect of modern networking paradigms. We approach this issue from a game theoretical perspective, considering networks where actors have social or economic interests modeled through a cooperative game. This allows us to define, for each network, a family of efficiency measures and another of vulnerability measures, parameterized by the game. The proposed measures use the within groups’ and the between groups’ Myerson values. These values, respectively, measure the portion of the classical Myerson allocation corresponding to the productivity of players and the part related to intermediation costs. Additionally, they indicate the portion of total centrality in social networks attributed to communication or betweenness. In our proposal, the efficiency of a network is the proportion of total productivity (or centrality) that players can retain using the network topology. Intermediation costs (and betweenness centrality) can be seen as a weakness with a negative impact. Therefore, we suggest calculating vulnerability as the proportion of expenses players incur in intermediation payments. We explore the properties of these measures and tailor them to various structures and specific games, also analyzing their asymptotic behavior.

Research on the influence of chain shareholder network on enterprise green innovation

Green innovation plays a pivotal role in the high-quality development of social economy, but its high risk and investment can daunt enterprises. As a result, business stakeholders often use interpersonal relationships to start to overcome boundaries to obtain and disseminate green innovation information and resources. Based on the number of green innovation patent applications from 2011 to 2020, this paper constructs an undirected weighted network of enterprise chain shareholders and empirically examines the influence of centrality and structural hole characteristics of social networks on green innovation. Additionally, a heterogeneity analysis is conducted from the perspectives of core-periphery structure and CEO overseas experience. The findings are beneficial for enterprises to have achieve a more comprehensive understanding of the role of their positional advantages in the network on green innovation and provide new ideas and directions for firms to improve their green innovation level.

Where do differences in theory of mind development come from? An agent-based model of social interaction and theory of mind

IntroductionWhere do individual differences in theory of mind (ToM) development come from? This is an open-ended issue in developmental psychology. Correlational research suggests social interaction as a cause. However, causal experiments controlling children's extended, real-life social interactions are impractical and unethical, so the links between social interaction and ToM remain only indirectly demonstrated. To shed light on how social interaction, in different degrees and forms, may influence ToM development, we conducted computational experiments using agent-based modeling.MethodsTo begin with, we simulated agents (hypothetically, children) and assigned them to 10 groups, where each group was manipulated to allow a different amount of social interaction, from 1 to 10. Within each group, agents randomly roamed and encountered one another and then interacted. If these interactions continued long enough, agents' ToM probabilistically improved.ResultsAs expected, results showed that the amount of social interaction strongly influenced how fast agents developed ToM, where more social interaction led to more rapid development. Given this confirmation of the assumed link, we more focally explored how other factors—such as having a prior, established social network or agents' network centrality—could influence the social interaction-ToM link. Then, we tested our model against real-world data from 84 deaf children and showed that our modeling results could explain the social interaction-ToM link observed in deaf children.DiscussionThese demonstrations illustrate how individual differences in ToM development may emerge and offer an explanation specifying a crucial mechanism for how all humans achieve ToM—faster or with delay—through processes of social interaction.

The Factors Influencing the Idea Recognition of Open Innovation Platforms: Take LEGO Ideas as an Example

With the influx of users posting their ideas to Open Innovation Platforms (OIPs), there has been widespread interest in how companies screen high-quality ideas. Most of the existing literature on idea quality focuses on the adoption of ideas by companies, while ignoring the recognition among users. Based on the ideas posted by users of LEGO Ideas, this study first analyzes the influence of users’ social learning and social network on their innovation ability, and then further investigates the influence of idea authors’ innovation ability and idea content’s characteristics on idea recognition based on signal theory. The findings suggest that more challenging social learning as well as weighted indegree and betweenness centrality of social network positively affect users’ innovation ability and weighted outdegree of social network negatively affects users’ innovation ability. Meanwhile, idea authors’ innovation ability and the number of pictures, text length, richness, popularity, and emotional polarity of idea content positively affect idea recognition. Distinct from the existing literature, this paper focuses on idea recognition among users, delves into the influence of social learning and social network on users’ innovation ability, and integrates the study of factors influencing users’ innovation ability into the study of factors influencing idea recognition.

Building rural community resilience: The case of gem extraction in flood-affected areas of Sri Lanka

Sri Lankan gem mining and extractive industry communities experience enormous amounts of devastation from mass flooding events. Despite the pressing importance of understanding Gem Mining Community Resilience (GMCR) vis-à-vis community flood disaster management, the topic remains under researched. In order to address this gap in the extant literature, this study investigates the resilience of Sri Lankan gem mining communities’ social support network legacies (mitigation and adaptation) in response to a 2017 mass flooding event. Survey data were collected from 167 flood-affected gem mining households (GMHs) in fifteen local administrative units (GNDs) located in two Sri Lankan divisional secretariat divisions (DSDs). Social Network Analysis (SNA) is deployed to investigate the changing contours of social support network centralities before, during, and after the flooding event. Results suggest that the social support network legacies of the surveyed communities evolved over different flood-inundation phases in different geographical settings, thus improving GMCR. Flood inundation depth impacted network activities as well. Multi-actor social support networks working in concert with social capital legacies have shaped the processes of resource mobilization and flood victim livelihood revivification. These mitigation and adaptation practices, along with a GMCR framework developed in the study, may impart lessons for local and national policy, as well as for gem mining communities in other developing countries.

The detection of self–group conflicts in exercise behaviors differs with social network centrality: ERP evidence

BackgroundThe influence of social norms on exercise behaviors has been explored in studies over the years. However, little is known about whether an individual’s role (central or peripheral) in his or her social network, which is associated with social skills, could shift his or her susceptibility to normative effects on exercise behaviors. To that end, event-related potentials (ERPs) were recorded to examine the underlying cognitive mechanism of the effects of network centrality on normative social influence. MethodsWe manipulated network centrality by assigning participants to exercise support groups, with group members who were their nominated friends (high centrality) or nonnominated classmates (low centrality). Participants were asked to evaluate their willingness to engage in various exercises, after viewing discrepant group ratings (peer influence) or not viewing (no-influence). ResultsPeer influence evoked a larger negative-going feedback-related negativity (FRN) wave, which was linked to automatic social conflict detection, and a larger positive-going P3 wave, which was linked to subsequent conformity behavioral changes. However, effects on the FRN, not the P3, were observed only in the high-centrality group. ConclusionOur results highlight the important roles of network centrality in encoding self–group exercise attitude discrepancy rather than in decision-making regarding exercise attitude adjustments. Interventions aimed at promoting exercise behaviors should be considered in a broader social environmental framework.

The influence of social network on innovation and entrepreneurship of economic and management college students based on adaptive particle swarm algorithm

Abstract Exploring the influence of social networks on innovation and entrepreneurship of economics and management college students to help them better enhance their innovation consciousness to face the future social workplace. In this paper, the particle swarm algorithm is optimized according to the strategy and parameter adaptive mechanism, and the SPS-PSO algorithm is used to initialize and update the population. The social network and innovation and entrepreneurship ability of university students in economics and management are introduced, and feature selection based on strategy and parameter adaptive particle swarm algorithm is utilized. The index mining of social network and innovation and entrepreneurship ability of college students is conducted, and the data analysis of innovation and entrepreneurship ability of college students in economics and management is carried out with the example of the University of S. From the social network, the percentages of those who agree with the validity of social network centrality, heterogeneity, dynamics and linkage strength are 71.56, 71.69%, 72.55% and 71.16%, respectively. From the perspective of innovation and entrepreneurship, the overall percentage of college students in economics and management who agreed was 69.27%. This indicates that the higher the integrity of the social network structure of economics and management college students, the more it can enhance innovation and entrepreneurship abilities and promote the cultivation of observation and judgment ability and divergent thinking abilities of economics and management college students.

Mapping analytical hierarchy process research to sustainable development goals: Bibliometric and social network analysis

The drive to achieve the Sustainable Development Goals (SDGs) becomes more urgent as the 2030 deadline draws near, increasing research in various sectors. Nevertheless, studies that systematically map Analytical Hierarchy Process (AHP) publications with the SDGs need to be more conspicuously lacking. Our study adds a new perspective to the field by creatively bridging this knowledge gap using the Elsevier SDG Mapping Initiative. To find research clusters, trends, and themes linked to SDGs and their connection to environmental sustainability, we thoroughly analyzed 29,897 publications from 2012 to 2022. The analysis showed that SDG 15, SDG 7, SDG 12, SDG 13, and SDG 11 were the top five SDGs, with an environmental focus among the 17 SDGs. These top SDGs had many clusters connected to them, illustrating various sustainability-related problems. The study also looked at connections between SDGs, the nations with the highest rates of productivity, the top contributors, and the journals with the highest citation counts. We discovered three separate SDG clusters using co-occurrence network analysis, each representing a different SDG. We discovered relevant SDGs using Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis and centrality indicators like eigenvector and betweenness. This novel method for publishing analysis combines an AHP focus that aligns with the SDGs with social network analysis and centrality metrics. Our research advances knowledge of how the AHP technique can assess initiatives supporting the SDGs. We offer essential insights into prioritizing sustainable development measures by identifying research clusters, trends, and issues related to environmental sustainability. This study highlights the subject's most important SDGs, productive nations, helpful organizations, and significant journals.

How social activities affect corporate credit behavior? – the mediating role of network centrality

PurposeSocial information is crucial to credit ratings and can improve the accuracy of the traditional credit assessment model. Drawing on the resource-based view (RBV) and social capital theory (SCT), this research explores the relationships between corporate social activities, network centrality and corporate credit behavior.Design/methodology/approachThe authors used social network analysis (SNA) and regression analysis to analyze the data collected from 14,544 enterprises on the Alibaba platform.FindingsThe results indicate that among the four types of social activities, the number of corporate questions and posts shows a positive relationship with credit behavior; while the number of corporate comments has negative relationship with credit behavior. Further, degree and betweenness centralities mediate the relationship between the number of corporate questions, posts and comments with credit behavior.Originality/valueThis study contributes to the literature on non-financial factors (soft information) by exploring the social behavioral factors related to corporate credit. In addition, this study offers a new theoretical lens and reasonable explanations for investigating the relationship between corporate social activities, network centrality and credit behavior from the perspective of the resource-based view, while most studies are predictive and methodological. Moreover, this study provides new insights for platforms to evaluate enterprise credit and for managers to improve credit behavior.

Social networks and bond covenants: evidence from China

ABSTRACT This paper explores the relationship between managerial social networks and bond covenants by using a sample of Chinese corporate bonds during 2016–2019. We find that firms with higher social network centrality issue corporate bonds with more covenant restrictions. This positive association is amplified in provinces with less developed financial systems and weaker legal protection of creditors. Our results are robust to several robustness tests and endogeneity considerations. Moreover, we examine the mechanisms relating social network centrality to bond covenants. The results show that social networks can reduce the information opacity and strengthen contract enforceability. We also find that the positive association between social network centrality and bond covenants is more pronounced in firms with lower prior but higher current social networks. Finally, this paper shows that the benefits of debt covenants in reducing credit spreads only exist in firms with higher social network centrality.

Exploring the technological position and role of vehicle navigation companies by using patent citation network

Autonomous driving is the future trend when it comes to the age of 5G at the end of 21st century, and vehicle navigation is the indispensable key technology. The companies with car navigation software and hardware business are facing fierce competition. Business operators must know the company's position and role in the industry, in order to adopt strategies to respond. In this study, two indicators are investigated using the social network analysis technics such as technological redundancy and network centrality in patent citation network (PCN). It is understood that technological redundancy is determined by technology knowledge status (TKS) as well as technology knowledge reliability (TKR). There are also four types of social network centrality: degree centrality, eigenvector centrality, closeness centrality, and betweenness centrality. In this study, an analysis of the technical position and role of individual companies in the industry will be presented by using the two indicators and the primary components. Moreover, technological redundancy is used to study the technical position and role of a company's unitary patent capability in the industry. This will reinforce the company's understanding, and then take strategies. By using this model, the study can effectively identify the technical position and role of the company and its unitary patent capability. Furthermore, the study also shows that based on this model, there is a total of three companies where positions and roles are as leading status in the vehicle navigation industry, i.e. Tomtom map company, Alpine and Asin AW two electronic companies. In addition, companies with unique technologies, such as Sirius Xm Radio, etc. can use their role and position to conduct merger and acquisition, collaboration, and alliance strategies.