Search Results (29)

The structure of a policy communication network shows the effect of policy communication on social media. Policies need to be dynamically adjusted during the implementation process, which may affect the policy’s interaction on social media. Based on the Policy Network Theory, this study explores the effects of policy communication changes on social media before and after the adjustment of China’s Mass Entrepreneurship and Innovation (MEI) Policy using Exponential Random Graph Models (ERGMs) analysis and community analysis. The study reveals that after the policy adjustment, the communication network structure indicated a significant increase in triangular configurations, yet the formation of edges remained constrained. Meanwhile, cross-community connections in the communication network decreased, with communities exhibiting localized contraction, and emotional polarization becoming more pronounced. These phenomena occurred because policy adjustments have boosted interaction levels through new incentive mechanisms, whereas the content and delivery methods of policy communication remain insufficiently engaging, which constrains relationship-building. Additionally, the policy’s evolution from a mobilization–participation model to a vertical governance paradigm has systematically reconfigured inter-community interaction patterns, resulting in structural transformations in cross-group information flows. To enhance the dissemination of policies on social media, it is recommended to intervene in the policy communication network structure through role embedding, shift from a reactive public sentiment management paradigm to proactive emotional governance, and strengthen policy communication strategies that emphasize emotional resonance. These measures can improve the effectiveness of policy communication and help address the challenges posed by emotional polarization and network fragmentation. Full article

Modern organizations operate not only through formal structures but also through informal networks, which play a critical role in fostering a resilient organization. This study focused on informal advice networks within organizations as a key mechanism for strengthening contextual resilience, one of the core components of organizational resilience. By analyzing the activation of informal advice networks, this study conceptualized advice-seeking networks as a critical informal system that enhances contextual resilience and examined the individual, structural, and attitudinal factors influencing their formation. Specifically, we hypothesized that employees with higher levels of Machiavellianism are more likely to engage in advice-seeking behaviors, whereas the relationship between Machiavellianism and advice-seeking behaviors is moderated by betweenness centrality and organizational commitment, such that the positive effect of Machiavellianism on advice-seeking is weaker when betweenness centrality or organizational commitment is high. To empirically test these hypotheses, we conducted a network survey of employees at the headquarters of a life insurance company in Seoul, South Korea, and analyzed the data using an Exponential Random Graph Model (ERGM). The findings provide empirical support for all hypotheses. Based on these results, we discussed the theoretical contributions and practical implications of the study, along with its limitations. Full article

During public health emergencies, studying the travel characteristics and influencing factors of shared bicycles during different time periods on weekdays can provide valuable insights for urban transportation planning and offer recommendations for bike-sharing systems (BSS) affected by such events. Utilizing bike-sharing data, this study initiated the analysis by scrutinizing the spatial flow patterns in the core area of Beijing, employing network indicators within the framework of complex network theory. Subsequently, influencing factors associated with bike-sharing trips were pinpointed using the exponential random graph model (ERGM). Using COVID-19 as an example, it examines the impact of public health emergencies on bike-sharing during multiple time periods. Supported by the network analysis method, our findings revealed that the majority of travel activities occurred between adjacent areas. Throughout weekdays, a consistent level of travel activity was observed, exhibiting distinct patterns during daytime and nighttime. The period from 4:00 to 8:00 emerged as the peak time, characterized by heightened traffic and temperature changes. Morning commuting extended until 8:00–12:00, followed by a transition period from 12:00–16:00. The most active travel time, encompassing various purposes, was identified as 16:00–20:00. Additionally, the presence of hospitals and train stations amplified travel within the pandemic-affected area. Finally, variants of ERGMs were employed to assess the influence of finance, shopping, dining, education, transportation, roads, and COVID-19 on bike-sharing activities. The road network emerged as the most critical factor, exhibiting a significant negative impact. Conversely, COVID-19 had the most pronounced positive influence, with transportation stops and educational institutions also contributing significantly in a positive manner. This research provides valuable transportation planning insights for addressing public health emergencies and promotes the effective utilization of bike-sharing systems. Full article

In light of the crucial role of collaborative R&D in advancing technology within the new energy vehicle industry, this study seeks to explore ways to overcome the barriers to technological innovation by establishing an effective collaborative innovation network. Utilizing joint patent-authorized data from China’s new energy vehicles between 2005 and 2019, the collaborative innovation network was developed, and the Exponential Random Graph Model (ERGM) was employed to analyze its formation and evolution mechanisms. The results indicate that the network has undergone significant expansion, closely linked to strong national policy support and the active involvement of innovation participants. The network exhibits effects of expansion, transfer, and closure. External attribute analysis revealed the Matthew effect and geographical compatibility effect and found that organizational compatibility tends to foster complementary cooperation. The findings offer insights into optimizing collaborative innovation networks in the NEVs industry and suggest strategies for policymakers and industry players to promote collaborative innovation. Full article

In the digital era, data-driven production organizes digital urban networks. This study explores the critical role of government policies in shaping these networks, focusing on China’s evolving policy contexts. While existing research has mainly emphasized qualitative analyses, this paper quantitatively assesses the impact of policy changes on digital urban networks, specifically through the lens of China’s online gaming industry. The study aimed to elucidate the relationship between the policy environment and digital urban networks. By examining China’s transition from protectionist to regulatory policies, this research employed a social network analysis and valued exponential random graph models (ERGMs) across two key phases: the competitive protection phase (2014–2017) and the systematic regulatory phase (2018–2022). The findings revealed a significant transformation in urban network structure, shifting from a centralized model dominated by a few core cities to a decentralized, multi-centered network. The key factors influencing this evolution include the institutional proximity and cross-regional collaborations. This study offers valuable insights into how policy shifts affect urban networks in the digital economy, contributing both theoretically and practically to future policy design. Full article

The poor response, adverse effects and drug resistance to treatment of acute myeloid leukemia (AML) have led to searching for safer and more effective therapeutic alternatives. We previously demonstrated that the alcoholic extract of Petiveria alliacea (Esperanza) has a significant in vitro antitumor effect on other tumor cells and also the ability to regulate energy metabolism. We evaluated the effect of the Esperanza extract in vitro and in vivo in a murine model of AML with DA-3/ER-GM cells. First, a chemical characterization of the extract was conducted through liquid and gas chromatography coupled with mass spectrometry. In vitro findings showed that the extract modulates tumor metabolism by decreasing glucose uptake and increasing reactive oxygen species, which leads to a reduction in cell proliferation. Then, to evaluate the effect of the extract in vivo, we standardized the mouse model by injecting DA-3/ER-GM cells intravenously. The animals treated with the extract showed a lower percentage of circulating blasts, higher values of hemoglobin, hematocrit, and platelets, less infiltration of blasts in the spleen, and greater production of cytokines compared to the control group. These results suggest that the antitumor activity of this extract on DA-3/ER-GM cells can be attributed to the decrease in glycolytic metabolism, its activity as a mitocan, and the possible immunomodulatory effect by reducing tumor proliferation and metastasis. Full article

Network anomaly detection solutions can analyze a network’s data volume by protocol over time and can detect many kinds of cyberattacks such as exfiltration. We use exponential random graph models (ERGMs) in order to flatten hourly network topological characteristics into a time series, and Autoregressive Moving Average (ARMA) to analyze that time series and to detect potential attacks. In particular, we extend our previous method in not only demonstrating detection over hourly data but also through labeling of nodes and over the HTTP protocol. We demonstrate the effectiveness of our method using real-world data for creating exfiltration scenarios. We highlight how our method has the potential to provide a useful description of what is happening in the network structure and how this can assist cybersecurity analysts in making better decisions in conjunction with existing intrusion detection systems. Finally, we describe some strengths of our method, its accuracy based on the right selection of parameters, as well as its low computational requirements. Full article

Global trade of renewable energy products has increased significantly in recent years. This paper constructs an analytical framework of a global trade network for renewable energy products based on bilateral trade data between 2009 and 2019. It analyses its structural evolution at the global and local levels and investigates the influencing factors of the network with the Exponential Random Graph Model. The empirical results indicate that countries in the trade network have become more closely connected, featuring a core-periphery and increasing reciprocity relationship. China, Germany, and Japan have remained in the position of core countries; China has especially been prominent among core countries. Our empirical results verify that the sender-receiver effects can explain the evolution of this global trade network. The empirical results also indicate that the climate change agreement network and the common border network have positive effects on the formation of the trade network. As regards political implications, the core countries in the trade network should optimize the layout of renewable energy development and improve infrastructure accordingly. Countries should also jointly build a more fair and reasonable multilateral system that fulfills their responsibilities. Full article

Innovation is the engine for the sustainable development of cities and regions, and an innovation perspective is used to study the collaborative innovation relationship between regional cities and the evolution mechanism of regional innovation network formation. Based on the social network analysis method and spatial analysis method, we explore the characteristics of the regional research cooperation network and its spatial pattern, as well as analyze the formation mechanism of the network using the exponential random graph model. The study finds that the scale of the regional innovation network is expanding, the overall network density is gradually increasing, but the nodes are loosely linked, and the regional innovation network is in the stage of deepening development. There is spatial heterogeneity in the regional innovation network, with a dumbbell-shaped distribution of “double-core dominance and central collapse”. During the formation of the regional innovation network, the endogenous structure of the innovation network is multi-connected under the path dependence, and the network development tends to be complicated; the economic strength and scientific research capability of cities and multi-dimensional proximity have a positive influence on the formation of the innovation network, and geographical proximity, social proximity and cognitive proximity can promote intra-regional innovation cooperation. Full article

The innovation ecosystem of the new energy vehicle industry is highly complex and spans several sectors; it is important to increase the resistance of the new energy vehicle industry network. This paper explores regional resilience in the context of China’s new energy vehicle industry. Using patent cooperation data from 2011 to 2021, we construct a three-stage urban collaborative innovation network and analyze its structural characteristics. We also develop a resilience evaluation index system to measure the resilience of the network and its nodes. Furthermore, we propose a framework for resilience analysis that operates at the network, community, and node levels. Through our analysis of the network’s resilience characteristics and evolution, we investigate the driving mechanisms behind its formation using the exponential random graph model (ERGM). Empirical results demonstrate that the urban collaborative innovation network is expanding and strengthening, with increased resilience and the ability to withstand uncertainty. Notably, the distribution of node resilience exhibits spatial heterogeneity, with cities in the eastern and central regions demonstrating higher resilience than those in other areas. Furthermore, the study finds that economic development and investment in education and technology can enhance network connections and resilience. Additionally, this paper reveals a strong dependence of inter-city cooperation on geographical proximity. Full article

Understanding the relationships within product co-purchasing is crucial for designing effective cross-selling and recommendation systems in e-commerce. While researchers often detect co-purchase rules based on product attributes, this study explores the influence of consumer behavior preferences and electronic word-of-mouth (eWOM) on co-purchase formation by analyzing the topological network structure of products. Data were collected from a major Chinese e-retailer and analyzed using an exponential random graph model (ERGM) to identify the factors affecting the formation of follow-up purchases between products: the role of topological structure, product category, and online product reviews. The results showed that the co-purchase network was a sparse small-world network, with a product degree of centrality that positively impacted its sales volume within the network, suggesting a concentration effect. Cross-category purchases significantly contribute to the formation of co-purchase relationships, with a differential homophily effect. Positive ratings and review volumes were found to be key factors impacting this co-purchase formation. In addition, a higher inconsistency of positive ratings among products decreases the likelihood of co-purchase. These findings contribute to the literature on eWOM and electronic networks, and have valuable implications for e-commerce managers. Full article

Between October 2021 and April 2022, 317 outbreaks caused by highly pathogenic avian influenza (HPAI) H5N1 viruses were notified in poultry farms in the northeastern Italian regions. The complete genomes of 214 strains were used to estimate the genetic network based on the similarity of the viruses. An exponential random graph model (ERGM) was used to assess the effect of ‘at-risk contacts’, ‘same owners’, ‘in-bound/out-bound risk windows overlap’, ‘genetic differences’, ‘geographic distances’, ‘same species’, and ‘poultry company’ on the probability of observing a link within the genetic network, which can be interpreted as the potential propagation of the epidemic via lateral spread or a common source of infection. The variables ‘same poultry company’ (Est. = 0.548, C.I. = [0.179; 0.918]) and ‘risk windows overlap’ (Est. = 0.339, C.I. = [0.309; 0.368]) were associated with a higher probability of link formation, while the ‘genetic differences’ (Est. = −0.563, C.I. = [−0.640; −0.486]) and ‘geographic distances’ (Est. = −0.058, C.I. = [−0.078; −0.038]) indicated a reduced probability. The integration of epidemiological data with genomic analyses allows us to monitor the epidemic evolution and helps to explain the dynamics of lateral spreads casting light on the potential diffusion routes. The 2021–2022 epidemic stresses the need to further strengthen the biosecurity measures, and to encourage the reorganization of the poultry production sector to minimize the impact of future epidemics. Full article

The characteristics and influence of the echo chamber effect (TECE) of health misinformation diffusion on social media have been investigated by researchers, but the formation mechanism of TECE needs to be explored specifically and deeply. This research focuses on the influence of users’ imitation, intergroup interaction, and reciprocity behavior on TECE based on the social contagion mechanism. A user comment–reply social network was constructed using the comments of a COVID-19 vaccine video on YouTube. The semantic similarity and Exponential Random Graph Model (ERGM) were used to calculate TECE and the effect of three interaction mechanisms on the echo chamber. The results show that there is a weak echo chamber effect (ECE) in the spread of misinformation about the COVID-19 vaccine. The imitation and intergroup interaction behavior are positively related to TECE. Reciprocity has no significant influence on TECE. Full article

Understanding the regularity and determinants of mobility is indispensable for the reasonable deployment of shared bicycles and urban planning. A spatial interaction network covering streets in Beijing’s six main districts, using bike sharing data, is constructed and analyzed. as Additionally, the exponential random graph model (ERGM) is used to interpret the influencing factors of the network structure and the mobility regularity. The characteristics of the spatial interaction network structure and temporal characteristics between weekdays and weekends show the following: the network structure on weekdays is obvious; the flow edge is always between adjacent blocks; the traffic flow frequently changes and clusters; the network structure on weekends is more complex, showing scattering and seldom changing; and there is a stronger interaction between blocks. Additionally, the predicted result of the ERGM shows that the influencing factors selected in this paper are positively correlated with the spatial interaction network. Among them, the three most important determinants are building density, housing prices and the number of residential areas. Additionally, the determinant of financial services shows greater effects on weekdays than weekends. Full article

Online medical consultation (OMC) allows doctors and patients to communicate with each other in an online synchronous or asynchronous setting. Unlike face-to-face consultations in which doctors are only passively chosen by patients with appointments, doctors engaging in voluntary online consultation have the option of choosing patients they hope to treat when faced with a large number of online questions from patients. It is necessary to characterize doctors’ preferences for patient selection in OMC, which can contribute to their more active participation in OMC services. We proposed to exploit a bipartite graph to describe the doctor–patient interaction and use an exponential random graph model (ERGM) to analyze the doctors’ preferences for patient selection. A total of 1404 doctor–patient consultation data retrieved from an online medical platform in China were used for empirical analysis. It was found that first, mildly ill patients will be prioritized by doctors, but the doctors with more professional experience may be more likely to prefer more severely ill patients. Second, doctors appear to be more willing to provide consultation services to patients from urban areas, but the doctors with more professional experience or from higher-quality hospitals give higher priority to patients from rural and medically underserved areas. Finally, doctors generally prefer asynchronous communication methods such as picture/text consultation, while the doctors with more professional experience may be more willing to communicate with patients via synchronous communication methods, such as voice consultation or video consultation. Full article