Network Relationships Research Articles

The Influence Mechanism of Online Social Network Relationships on Sustainable Entrepreneurial Success

Affected by economic uncertainty, the success rate of sustainable entrepreneurship is not optimistic at present. Effectively improving the success rate of sustainable entrepreneurship is of great significance for ensuring and maintaining sustainable development for long-term socioeconomic and environmental sustainability. Based on social network theory, entrepreneurial capital theory, psychological capital theory, and the literature on sustainable entrepreneurship, this study explores the influence mechanism of online social network relationships on sustainable entrepreneurial success from the perspective of those relationships, dividing entrepreneurs’ abilities into an online relationship-building ability and online relationship coordination ability. We used an empirical study with 346 valid questionnaires. The results of a PLS-SEM analysis show that online social network relationships have a significant impact on sustainable entrepreneurial capital, in which online relationship-building capacity has a similar impact on both sustainable entrepreneurial resource integration and acquisition; online relationship-building capacity can positively affect both transactional and interpersonal psychological capital; sustainable entrepreneurial resource integration can be more effective than resource acquisition in promoting sustainable entrepreneurial success through the use of social media; and transactional psychological capital has a more significant impact on sustainable entrepreneurial success than does interpersonal psychological capital. This study provides new insights into how entrepreneurs can fully utilize their online social network relationships to achieve entrepreneurial success.

Impact of Rural E-Commerce Participation on Farmers’ Household Development Resilience: Evidence from 1229 Farmers in China

This paper investigates the impact of e-commerce participation on household development resilience using a sample of 1229 households in the Shandong and Shaanxi provinces of China in 2022. It constructs the developmental resilience index of farm households from three dimensions of economy, society and culture using the entropy method, and establishes a counterfactual framework using the Propensity Score Matching (PSM) method. The results suggest that participation in e-commerce has a significant and positive impact on farming household development resilience. The PSM method estimates that participation in e-commerce increases the developmental resilience of farming households by 9.63% compared to non-participation, with economic, social, and cultural resilience increasing by 9.29%, 9.84%, and 9.92%, respectively. The robustness test results confirm the findings. Further analysis reveals that participation in e-commerce enhances farm household development resilience through three mechanisms: improving economic efficiency, network relationship linkage, and risk appetite. Heterogeneity analysis shows that the impact of e-commerce participation on household development resilience varies among farmers with different endowment constraints. In particular, farmers with more years of education and cooperative members benefit more from e-commerce participation, especially live and platform e-commerce.

Urgency of Social Capital to Improve the Resilience of Independent Oil Palm Farmers in Managing Oil Palm Plantations in Peat Areas: Lessons from Indonesia

Objective: This research focuses on studying the social capital of oil palm farmers in Sidomulyo Village, Wanaraya District, Barito Kuala District, South Kalimantan Province. Theoretical Framework: The study aims to achieve two main objectives: firstly, to identify the social capital possessed by independent oil palm farmers; secondly, to describe how they maintain this social capital. Method: Qualitative research methods were employed to gain a profound understanding of the social capital that supports the success of independent smallholders in managing oil palm plantations. The study was conducted in Sido Mulyo village, Wanaraya district, Barito Kuala Regency, South Kalimantan Province. Informants, serving as sources of information, were selected using purposive sampling techniques. Data collection involved observation, in-depth interviews with selected informants, and daily records, which played a crucial role in this research. The obtained data will be subjected to phenomenological analysis. The study revealed two key findings: first, various forms of social capital, and second, how this social capital is utilized. Results and Discussion: The identified forms of social capital include: (1) the social networks established by farmers with middlemen to sell palm oil to processing companies, (2) their membership in peasant groups, and (3) the work ethic exhibited by farmers as transmigrants. The utilization of social capital for their progress as farmers involves: (1) maintaining relationships in social networks to secure favorable palm oil prices and access to financial capital, (2) leveraging farmer groups to access fertilizer subsidies and protect against the threat of land fires, and (3) employing social norms to foster mutual assistance in preserving road access to oil palm plantations. Research Implications: This research contributes to understanding the various capitals owned by smallholders in oil palm plantation exploitation and the use of social capital for their success in plantation management palm oil. This model can be a reference for communities who want to get similar success in the development of oil palm plantations and can be done also in other locations that have the same land structure. Originality/Value: Social capital plays a crucial role in the success of independent farmers managing oil palm plantations. Several conclusions have been drawn regarding social capital in the management of oil palm plantations by independent smallholders. Firstly, the forms of social capital owned by independent oil palm plantation farmers include: (1) social networks established by farmers with middlemen for selling oil palm to processing companies, (2) membership in farmer groups, (3) the work ethic possessed by farmers as transmigrants, and (4) the social norms embraced by transmigrant farmers. Secondly, the utilization of social capital for their progress as farmers is carried out through: (1) maintaining relationships in social networks to secure palm oil prices and access capital, (2) utilizing farmer groups to access fertilizer subsidies and safeguard against the threat of land fires, and (3) applying social norms to foster mutual assistance in maintaining oil palm plantation infrastructure and addressing and preventing palm oil plantation fires.

Assessing resilience to systemic risks across interbank credit networks using linkage-leverage analysis: Evidence from Japan

The banking crisis started in March 2023 was triggered by disproportion of deposit liability and assets. Applying balance sheets from 1999 to 2022, from the Japanese Bank Association, this study scrutinizes the durability of high-priority banking systems against damaging events in that industry. Eyeing the unique high-level savings in Japanese banking, this study investigates the impact of disproportion of deposit liability and assets on the financial stability of interbank credit networks through a linkage-leverage analysis. This study presents a novel framework that combines actor-network theory, network optimization, and bilateral risk exposure approaches into linkage-leverage analysis. The results in this study show that the bilateral risk exposure during a subset of years is smaller than other years, which indicates that Japanese banks minimize the risk exposure by shifting the network relationship. Finally, this study provides supporting evidence to the new paradigms on developing more stable financial systems.

Invisible Iterations: How Formal and Informal Organization Shape Knowledge Networks for Coordination

AbstractThis study takes a network approach to investigate coordination among knowledge workers as grounded in both formal and informal organization. We first derive hypotheses regarding patterns of knowledge‐sharing relationships by which workers pass on and exchange tacit and codified knowledge within and across organizational hierarchies to address the challenges that underpin contemporary knowledge work. We use survey data and apply exponential random graph models to test our hypotheses. We then extend the quantitative network analysis with insights from qualitative interviews and demonstrate that the identified knowledge‐sharing patterns are the micro‐foundational traces of collective coordination resulting from two underlying coordination mechanisms which we label ‘invisible iterations’ and ‘bringing in the big guns’. These mechanisms and, by extension, the associated knowledge‐sharing patterns enable knowledge workers to perform in a setting that is characterized by complexity, uncertainty and ambiguity. Our research contributes to theory on the interplay between formal and informal organization for coordination by showing how self‐directed, informal action is supported by the formal organizational hierarchy. In doing so, it also extends understanding of the role that hierarchy plays for knowledge‐intensive work. Finally, it establishes the collective need to coordinate work as a previously overlooked driver of knowledge network relationships and network patterns.

GAT TransPruning: progressive channel pruning strategy combining graph attention network and transformer.

Recently, large-scale artificial intelligence models with billions of parameters have achieved good results in experiments, but their practical deployment on edge computing platforms is often subject to many constraints because of their resource requirements. These models require powerful computing platforms with a high memory capacity to store and process the numerous parameters and activations, which makes it challenging to deploy these large-scale models directly. Therefore, model compression techniques are crucial role in making these models more practical and accessible. In this article, a progressive channel pruning strategy combining graph attention network and transformer, namely GAT TransPruning, is proposed, which uses the graph attention networks (GAT) and the attention of transformer mechanism to determine the channel-to-channel relationship in large networks. This approach ensures that the network maintains its critical functional connections and optimizes the trade-off between model size and performance. In this study, VGG-16, VGG-19, ResNet-18, ResNet-34, and ResNet-50 are used as large-scale network models with the CIFAR-10 and CIFAR-100 datasets for verification and quantitative analysis of the proposed progressive channel pruning strategy. The experimental results reveal that the accuracy rate only drops by 6.58% when the channel pruning rate is 89% for VGG-19/CIFAR-100. In addition, the lightweight model inference speed is 9.10 times faster than that of the original large model. In comparison with the traditional channel pruning schemes, the proposed progressive channel pruning strategy based on the GAT and Transformer cannot only cut out the insignificant weight channels and effectively reduce the model size, but also ensure that the performance drop rate of its lightweight model is still the smallest even under high pruning ratio.

Does ecological network affect the construction land spatial development intensity? -An empirical study based on the main urban area of Jinan, China

The ecological network as the cornerstone of urban ecological security protection, understanding the interaction between ecological network and construction land spatial development intensity (CLSDI), which is crucial for guiding the orderly utilization of territorial space and promoting regional sustainable development. However, the information on the complex spatial and temporal interactions between ecological networks and CLSDI has not yet been adequately studied. This study takes the main urban area of Jinan City as an example, and explores the spatial and temporal coupling relationship and change rule of ecological network and CLSDI during 2006–2018 through spatial multiple buffer analysis tools, and proposes the optimization path of construction land spatial development under the perspective of ecological network. The results showed that: from 2006 to 2018, there is a significant spatial effect between CLSDI and ecological network elements; within the buffer zone of 300–3000 m for ecological sources and nodes, CLSDI rises with the increase of buffer zone distance; while the ecological corridor exhibits decreased CLSDI within the 500-m buffer zone, the 500–3000-m CLSDI tends to increase. Based on the empirical analysis, the optimization path of construction land spatial development is proposed from three aspects: ecological source, corridor and node. The 300 m control range of the ecological source is designated as a “specific protection zone”, and strictly control the increase of the CLSDI within the zone; the ecological corridor is set up with differential protection and utilization zoning, and CLSDI and activity types are controlled in a hierarchical and classified manner; the CLDSI around the ecological nodes is strictly controlled in order to prevent encroachment of construction activities on the nodes. This study provides a reference value for constructing a territorial spatial pattern coordinated with ecological protection and development of construction.

Spatiotemporal characteristics analysis of multi-factorial air pollution in the Jing-Jin-Ji region based on improved sequential ICI method and novel grey spatiotemporal incidence models

Air pollution shares the attributes of multi-factorial influence and spatiotemporal complexity, leading to air pollution control assistance models easily falling into a state of failure. To address this issue, we design a framework containing improved data fusion method, novel grey incidence models and air pollution spatiotemporal analysis to analyze the complex characteristics of air pollution under the fusion of multiple factors. Firstly, we improve the existing data fusion method for multi-factor fusion. Subsequently, we construct two grey spatiotemporal incidence models to examine the spatiotemporal characteristics of multi-factorial air pollution in network relationships and changing trends. Furthermore, we propose two new properties that can manifest the performance of grey incidence analysis, and we provide detailed proof of the properties of the new models. Finally, in the Jing-Jin-Ji region, the novel models are used to study the network relationships and trend changes of air pollution. The findings are as follows: (1) Two highly polluted belts in the region require attention. (2) Although the air pollution network under multi-factorial fusion obeys the first law of geography, the network density and node density exhibit significant variations. (3) From 2013 to 2021, all pollutants except O3 show improvement. (4) Recommendations for responses are presented based on the above-mentioned results. (5) The parameter analyses, model comparisons, Monte Carlo experiments and model feature summaries illustrate that the proposed models are practical, interpretable and considerably outperform various prevailing competitors with remarkable universality.

WEB OF TRANSFORMATIONS: impact of multicomponent intervention on the relationship between sociodemographic indicators, changes in body composition, cardiorespiratory fitness and biochemical markers in adolescents with overweight and obesity

ABSTRACT The study aims to analyze the relationships between changes after multicomponent intervention in sociodemographic indicators, body composition, cardiorespiratory fitness and biochemical markers in overweight/obese adolescents. Quasi-experimental study with 33 overweight/obese adolescents (17 in the intervention group (IG) and 16 in the control group (16)), in which the GI participated in the multicomponent intervention for 24 weeks. Sociodemographic indicators, body composition, cardiorespiratory fitness and biochemical markers were evaluated. Network analysis was performed using JASP software. In GI, the reduction in %BF proved to be the variable with greater connectivity and strength in the network compared to the control network. Changes in %BF were related to changes in ACR, BMI and leptin. It is concluded that the reduction in %BF is the most important variable in network relationships after the intervention, suggesting that the greater the reduction in %BF, the greater the effect on variables such as BMI, ACR and leptina.

Amendments affect the community assembly and co-occurrence network of microorganisms in Cd and Pb tailings of the Eucalyptus camaldulensis rhizosphere

Mining tailings containing large amounts of Pb and Cd cause severe regional ecosystem pollution. Soil microorganisms play a regulatory role in the restoration of degraded ecosystems. The remediation of heavy metal-contaminated tailings with amendments and economically valuable Eucalyptus camaldulensis is a research hotspot due to its cost-effectiveness and sustainability. However, the succession and co-occurrence patterns of these microbial communities in this context remain unclear. Tailing samples of five kinds of Cd and Pb were collected in E. camaldulensis restoration models. Physicochemical properties, the proportions of different Cd and Pb forms, microbial community structure, and the co-occurrence network of rhizosphere tailings during different restoration process (organic bacterial manure, organic manure, inorganic fertilizer, bacterial agent) were considered. Organic and organic bacterial manures significantly increased pH, cation exchange capacity, and the proportion of residual Pb. Still, there was a significant decrease in the proportion of reducible Pb. The changes in microbial communities were related to physicochemical properties and the types of amendments. Organic and organic bacterium manures decreased the relative abundance of oligotrophic groups and increased the relative abundance of syntrophic groups. Inorganic fertilizers and bacterial agents decreased the relative abundance of saprophytic fungi. B. subtilis would play a better role in the environment improved by organic manure, increasing the relative abundance of beneficial microorganism and reducing the relative abundance of pathogenic microorganism. pH, cation exchange capacity, and the proportion of different forms of Pb were the main factors affecting the bacterial and fungi variation. All four amendments transformed the main critical groups of the microbial network structure from acidophilus and pathogenic microorganisms to beneficial microorganisms. Heavy metal-resistant microorganisms, stress-resistant microorganisms, beneficial microorganisms that promote nutrient cycling, and copiotrophic groups have become critical to building stable rhizosphere microbial communities. The topological properties and stability of the rhizosphere co-occurrence network were also enhanced. Adding organic and organic bacterium manures combined with E. camaldulensis to repair Cd and Pb tailings improved (1) pH and cation exchange capacity, (2) reduced the biological toxicity of Pb, (3) enhanced the stability of microbial networks, and (4) improved ecological network relationships. These positive changes are conducive to the restoration of the ecological functions of tailings.

Synergistic regional emission reductions in China: Network evolution, spatial and temporal characteristics, and driving factor

Synergistic regional emission reduction (SRER) is an important part of the strategy to achieve carbon neutrality. In this paper, a system efficiency value-added model is proposed to measure the strength of SRER network relationships for 40,186 sets of city pairs and the SRER levels of 284 cities in China from 2011 to 2021. Subsequently, the evolution characteristics of the SRER network, the spatial and temporal characteristics of urban SRER levels, and the driving factors of the SRER network evolution are further analyzed. The results are as follows: SRER network is shifting from a fan-shaped network pattern of “three-region clustering and sporadic coverage” to a shuttle-shaped nested network pattern of “east-west linkage and multi-region support”. Time series analysis found that urban SRER level in China showed fluctuating growth characteristics during the study period. Spatial analysis reveals that the spatial autocorrelation of urban SRER level is decreasing, and the spatial distribution range is expanding towards the southwest. Emission reduction comparative advantages (CAER), green innovation division of labor (GIDL) and urban economic linkages (LCEL) are driver factors of SRER network evolution. This study proposes an evaluation method for the urban SRER level from network relationships perspective. Subsequently, network evolution characterization, spatial and temporal characterization, and driving factor analysis were conducted. The results of the study provide important theoretical guidance and inspiration for grasping the current status and enhancement paths of China's urban SRER level.

Understanding rural social networks addressing adverse childhood experiences: A case study of the San Luis Valley.

The purpose of this study is to assess a cross-sector, interorganizational network addressing adverse childhood experiences (ACEs) in a rural Colorado community. We characterize the organizations in the network, assess their awareness of ACEs, and evaluate how they participate in the network. We also assess the network health. Employing a social network analysis approach, we collected survey data from 45 organizations that support young children and their families, including nonprofits, health care clinics, and early childhood education centers, among others. On average, nonprofit organizations had relationships with a greater percentage of network members than other types of organizations. Network members engaged in relationships focused on a wide range of activities (e.g., client assessments, sharing information, providing services), with some organizational types leading the network in certain activities. Scores across all dimensions of trust and value were above 3 (range: 2.1-3.8), which is advantageous for a network and network relationships existed across a range of relational intensities (from awareness to organizational integration). Nonprofit organizations that reported high levels of connectedness in the network were able to effectively mobilize the ACEs network. Health clinics participated in a greater share of relationships involving assessment, service provision, and tool sharing than other types of organizations. As such, health care clinics may serve as leaders in directly serving children and families experiencing ACEs in rural communities. The rural context may also explain high levels of trust and value, which can serve as assets for future network development and mobilization. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Social Network Analysis: A Survey on Process, Tools, and Application

Due to the explosive rise of online social networks, social network analysis (SNA) has emerged as a significant academic field in recent years. Understanding and examining social relationships in networks through network analysis opens up numerous research avenues in sociology, literature, media, biology, computer science, sports, and more. Therefore, certain studies review and discuss some research verticals of SNA, such as viral marketing, information diffusion, clustering, link prediction, and so on, to provide background knowledge and understanding. These studies still lack the SNA process, tools, and practical aspects in multidisciplinary applications. Inspired by these facts, we have discussed the background, process, tools, and application of SNA. First, we have presented a detailed description of the SNA process. Thereafter, we presented a comparative analysis of SNA tools and languages. Finally, we have discussed the various applications corresponding to SNA research verticals.

Dynamic Bayesian-Network-Based Approach to Enhance the Performance of Monthly Streamflow Prediction Considering Nonstationarity

In recognizing the pervasive nonstationarity of hydrometeorological variables, a paradigm shift towards alternative analytical methodologies is imperative for refining hydroclimatic data modeling and prediction. We introduce a novel approach leveraging nonstationary Graphical Modeling and Bayesian Networks (NGM-BNs) tailored for hydrometeorological applications. Demonstrated through monthly streamflow forecasting in the Kashgar River Basin of China, our method illuminates the temporal evolution of network relationships, underscoring the dynamism inherent in both input variables and modeling parameters. The key to our approach is identifying the most suitable time horizon (MST) for model updates, which is intricately problem-specific and crucial for peak performance. This methodology not only unveils changing predictor significance across varying flow conditions but also elucidates the fluctuating temporal links between variables, especially under the lens of climate change, for instance, the growing impact of snowmelt on the Kashgar Basin’s streamflow. Compared to stationary counterparts, our nonstationary Bayesian framework excels in capturing extreme events by adeptly accommodating temporal shifts, outperforming traditional models including both stationary and nonstationary variants of Support Vector Regression (SVR) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS).

A muti-modal feature fusion method based on deep learning for predicting immunotherapy response

Immune checkpoint therapy (ICT) has greatly improved the survival of cancer patients in the past few years, but only a small number of patients respond to ICT. To predict ICT response, we developed a multi-modal feature fusion model based on deep learning (MFMDL). This model utilizes graph neural networks to map gene-gene relationships in gene networks to low dimensional vector spaces, and then fuses biological pathway features and immune cell infiltration features to make robust predictions of ICT. We used five datasets to validate the predictive performance of the MFMDL. These five datasets span multiple types of cancer, including melanoma, lung cancer, and gastric cancer. We found that the prediction performance of multi-modal feature fusion model based on deep learning is superior to other traditional ICT biomarkers, such as ICT targets or tumor microenvironment-associated markers. In addition, we also conducted ablation experiments to demonstrate the necessity of fusing different modal features, which can improve the prediction accuracy of the model.

Node2Vec and Machine Learning: A Powerful Duo for Link Prediction in Social Network

Link prediction in social networks is a challenging task that attempts to uncover hidden linkages and forecast future connections. The link prediction problem is addressed in this research article by utilizing the capabilities of Node2Vec and machine learning algorithms. To learn high-dimensional node representations that capture both local and global network structures, the Node2Vec technique is used. Then, in order to forecast potential connections, these node embedding’s are put into various machine learning models. Two real-world social network datasets are used to test the suggested methodology, and the findings show a considerable improvement in link prediction accuracy. It achieves a deeper comprehension of the hidden relationships in social networks by fusing the semantic richness of Node2Vec embedding’s with the predictive powers of machine learning methods. The results of this study extend link prediction approaches in social networks by revealing hidden ties and providing insightful predictions for upcoming connections. The suggested method indicates the potential for real-world applications in a number of fields, including recommender systems, targeted advertising, and social influence studies.

Investigating the Transition Upgrading Paths of Manufacturing Enterprises From an Energy Source Perspective

As the manufacturing industry is the foundation of the national economy, many countries have made concerted efforts to promote the transition upgrading of manufacturing enterprises from political, industrial, and academic circles. This study employed content analysis to explore the transition upgrading factors for manufacturing enterprises from an energy source perspective. With the help of FsQCA (Fuzzy-sets qualitative comparative analysis), which can mine multivariate combination relationships using a small number of samples, this study quantitatively calculated the transition paths and energy source combinations of manufacturing enterprises. Three transition upgrading paths were proposed, including an innovation-oriented path, a business-oriented path, and an efficiency-oriented path. The results indicate that manufacturing enterprises can realize transition upgrading based on three key methods, including innovation resources and innovation activities, strategic layout, and production operation + network relationship (peripheral). Such a finding validated the rationality of the theoretical path design and improved the research depth and precision. Multiple case analyses of Huawei, Haier, and Lenovo reveal that manufacturing enterprises used three ways to promote transition upgrading: the joint action of innovation activities and innovation resources; the uni-directional causal effect of strategic layout adjustment to increase market value; and the coordination and matching role of network relations dominated by production and operation. We conclude that this study provides an effective decision-making basis and management reference for manufacturing enterprises, so that enterprises can choose an appropriate energy accumulation method and scientifically realize the transition upgrading.

A Multiinterest and Social Interest-Field Framework for Financial Security

Online payment has become an influential method of transaction. While improving convenience, this method of payment also brings great financial risks. Prevalidating transactions can be effective in reducing the number of frauds. For this reason, recommendation algorithms have been introduced to measure the credibility of transactions by predicting users’ ratings of items. However, most algorithms deal with the relationships in social networks without distinction, mixing positive and negative information into the recommender system, which brings huge noise. And, they only generate a single interest representation for each user to measure the similarity between users and spread interest, ignoring the diversity of user interests. Moreover, they did not consider the propagation of different interests would be different. In this article, we propose a multiinterest and social interest-field framework (MISIF) for social recommendations in financial security, which introduces capsule networks into social recommendation and extends the traditional single-interest representation to user multiinterest embedding by dynamic routing (DR) and other methods to improve the expressiveness of user embedding. After that, we construct social interest fields to integrate social interests based on multiinterest embedding, which alleviates the noise in social networks and user data sparsity problems. Finally, we aggregate user multiinterest embedding and additional information through neural networks to obtain the final prediction scores. Experiments with three publicly available datasets show that our proposed MISIF framework outperforms the state-of-the-art social recommendation methods.

Understanding the Rivalry Among Telecommunication Companies in the Cross-Border Patent Infringement: An Evolutionary Social Network Analysis

Understanding the rivalry in infringement-litigation dynamics is critical for firms in the telecommunication industry. While the number of cross-border patent disputes in these years has surged owing to the globalized economy, the existing literature primarily focuses on patent disputes among telecommunication companies in the national jurisdiction. Hence, to capture the rivalry in the telecommunication industry more comprehensively, we utilize the evolutionary social network to explore the patent dispute data in the International Trade Commission (ITC), which is a crucial legal battleground for enterprises with international business. Through developing a dynamic litigation network and discussing the possible role of the positions in compliance with their interacting relationship, we illustrated that companies involved in ITC investigations can be segmented into key players, frequent respondents, strategic applicants, as well as peripheral enterprises, and the litigation strategies implicated behind four groups of firms stress the strategic nature of ITC investigations. According to the findings, this study contributes to the literature on patent litigation in the telecommunication field and social network relationships. In addition, this paper suggests that telecommunication companies should develop litigation strategies based on their positions and roles in the competition network, and policymakers need to have a deeper understanding of the competition practices in the global legal context.

Potential mechanism of ginseng in the treatment of periodontitis based on network pharmacology and molecular docking.

To explore the mechanism of ginseng in the treatment of periodontitis based on network pharmacology and molecular docking technology. Potential targets of ginseng and periodontitis were obtained through various databases. The intersection targets of ginseng and periodontitis were obtained by using VENNY, the protein-protein interaction network relationship diagram was formed on the STRING platform, the core target diagram was formed by Cytoscape software, and the ginseng-active ingredient-target network diagram was constructed. The selected targets were screened for gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. The core targets of ginseng's active ingredients in treating periodontitis were analyzed by molecular docking technique. The 22 ginseng's active ingredients, 591 potential targets of ginseng's active ingredients, 2 249 periodontitis gene targets, and 145 ginseng-periodontitis intersection targets were analyzed. Ginseng had strong binding activity on core targets such as vascular endothelial growth factor A and epidermal growth factor receptor, as well as hypoxia induced-factor 1 (HIF-1) signaling pathway and phosphatidylinositol 3-kinase-protein kinase B (PI3K-Akt) signaling pathway. Ginseng and its active components can regulate several signaling pathways such as HIF-1 and PI3K-Akt, thereby indicating that ginseng may play a role in treating periodontitis through multiple pathways.