Privacy-Centric AI and IoT Solutions for Smart Rural Farm Monitoring and Control.

The integration of artificial intelligence (AI) and the Internet of Things (IoT) in agriculture has significantly transformed rural farming. However, the adoption of these technologies has also introduced privacy and security concerns, particularly unauthorized breaches and cyber-attacks on data collected from IoT devices and sensitive information. The present study addresses these concerns by developing a comprehensive framework that provides practical, privacy-centric AI and IoT solutions for monitoring smart rural farms. This is performed by designing a framework that includes a three-phase protocol that secures data exchange between the User, the IoT Sensor Layer, and the Central Server. In the proposed protocol, the Central Server is responsible for establishing a secure communication channel by verifying the legitimacy of the IoT Sensor devices and the User and securing the data using rigorous cryptographic techniques. The proposed protocol is also validated using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The formal security analysis confirms the robustness of the protocol and its suitability for real-time applications in AI and IoT-enabled smart rural farms, demonstrating resistance against various attacks and enhanced performance metrics, including a computation time of 0.04 s for 11 messages and a detailed search where 119 nodes were visited at a depth of 12 plies in a mere search time of 0.28 s.

A Novel Hybrid Convolutional Neural Network- and Gated Recurrent Unit-Based Paradigm for IoT Network Traffic Attack Detection in Smart Cities.

Internet of Things (IoT) devices within smart cities, require innovative detection methods. This paper addresses this critical challenge by introducing a deep learning-based approach for the detection of network traffic attacks in IoT ecosystems. Leveraging the Kaggle dataset, our model integrates Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to capture both spatial and sequential features in network traffic data. We trained and evaluated our model over ten epochs, achieving an impressive overall accuracy rate of 99%. The classification report reveals the model's proficiency in distinguishing various attack categories, including 'Normal', 'DoS' (Denial of Service), 'Probe', 'U2R' (User to Root), and 'Sybil'. Additionally, the confusion matrix offers valuable insights into the model's performance across these attack types. In terms of overall accuracy, our model achieves an impressive accuracy rate of 99% across all attack categories. The weighted- average F1-score is also 99%, showcasing the model's robust performance in classifying network traffic attacks in IoT devices for smart cities. This advanced architecture exhibits the potential to fortify IoT device security in the complex landscape of smart cities, effectively contributing to the safeguarding of critical infrastructure.

Co-Design Dedicated System for Efficient Object Tracking Using Swarm Intelligence-Oriented Search Strategies.

The template matching technique is one of the most applied methods to find patterns in images, in which a reduced-size image, called a target, is searched within another image that represents the overall environment. In this work, template matching is used via a co-design system. A hardware coprocessor is designed for the computationally demanding step of template matching, which is the calculation of the normalized cross-correlation coefficient. This computation allows invariance in the global brightness changes in the images, but it is computationally more expensive when using images of larger dimensions, or even sets of images. Furthermore, we investigate the performance of six different swarm intelligence techniques aiming to accelerate the target search process. To evaluate the proposed design, the processing time, the number of iterations, and the success rate were compared. The results show that it is possible to obtain approaches capable of processing video images at 30 frames per second with an acceptable average success rate for detecting the tracked target. The search strategies based on PSO, ABC, FFA, and CS are able to meet the processing time of 30 frame/s, yielding average accuracy rates above 80% for the pipelined co-design implementation. However, FWA, EHO, and BFOA could not achieve the required timing restriction, and they achieved an acceptance rate around 60%. Among all the investigated search strategies, the PSO provides the best performance, yielding an average processing time of 16.22 ms coupled with a 95% success rate.

Analysis of retail sector research evolution and trends during COVID-19.

The purpose of this study is to analysis the evolution of the retail sector during the COVID-19 period and to identify future research issues. Scopus databases were searched for articles published in English between 2020 and 2022 to discover current trends and concerns in the retail industry. A total of 1071 empirical and nonempirical studies were compiled as a result of the evaluation process. During the study period, the number of articles published in scientific journals increased exponentially, indicating that the research topic is still in the developmental phase. It also highlights the most important research trends, allowing numerous new research lines to be proposed via visual mapping of Thematic Maps. This study makes an important contribution to the field of the retail sector, providing a comprehensive overview of the field's evolution and current status, as well as a comprehensive, synthesized, and organized summary of the various perspectives, definitions, and trends in the field.

Scientometric analysis of highly cited papers on avascular necrosis of the femoral head from 1991 to 2022.

INTRODUCTION: A highly cited paper (HCP) is considered a landmark that can influence both research and clinical practice. The characteristics of HCPs in avascular necrosis of the femoral head (AVNFH) were identified and the research status was explored in a scientometric analysis. METHODS: The present bibliometric analysis were based on the Scopus database from 1991 to 2021. Microsoft Excel and VOSviewer were used for co-authorship, co-citation, and co-occurrence analysis. From 8496 papers, only 2.9% (244) were HCPs, with 200.8 citations registered per article. RESULTS: Of the HCPs, 11.9% and 12.3% were externally funded and had international collaboration, respectively. These were published in 84 journals by 1625 authors from 425 organizations of 33 countries. The USA, Japan, Switzerland, and Israel were the leading countries.The lead research organizations were Sinai Hospital and John Hopkins University (USA). The most impactful organizations were University of Arkansas for Medical Science, and Good Samaritan Hospital (USA). R.A. Mont (USA) and K.H. Koo (South Korea) were the most prolific contributing authors, while R. Ganz (Switzerland) and R.S. Weinstein (USA) registered the most impactful contributions. The most prolific publishing journal was the Journal of Bone and Joint Surgery. CONCLUSION: The HCPs contributed to the knowledge of AVNFH by examining research perspectives and identifying important subareas through keyword analysis. LEVEL OF EVIDENCE: Not applicable. TRIAL REGISTRATION: Not applicable.

Scientometric analysis of Indian Orthopaedic Research in the last two decades.

PURPOSE: This study aimed to examine India's orthopaedic research output during 2002-2021 to analyze the research characteristics and publication performances of leading organizations, authors, and cities, the core journals publishing research, broad subject areas, sub-specialties, and the classification by anatomical location, the subject areas of research using major keywords and the sources of funding and the extent of international collaboration. METHODS: India's orthopaedic publications data was identified and downloaded from the Scopus database ( https://www.scopus.com ) using a well-defined search strategy and keywords. RESULTS: India's 4606 publications grew at a 20.8% annual growth rate and averaged 11.3 citations per paper. The 10.4% and 16.3% share of India's papers received external funded support and were involved in international collaboration. The USA and UK (31.8% and 21.3%) represent the highest collaborative share in India's international collaborative publications. AIIMS-New Delhi and PGIMER-Chandigarh produced a larger proportion of articles (5.2% and 4.3%) among contributing organizations. In terms of authors, R. Vaishya and S. Rajasekaran are the most productive ones, contributing 1.6% and 1.1% share respectively. Clinical studies, paediatric sub-specialty, and knee & leg anatomical location accounted for the largest share of papers (32.2%, 10.8%, and 7.5%).The most frequent keywords co-occurrences were "Orthopaedic Surgery," "Hydroxyapatite," "Biocompatibility," "Orthopaedic Procedures," "Bone," "Surgical Techniques," "Biomaterials," and "Osteosynthesis." CONCLUSION: This study revealed the characteristics and trends of research and core publications from Indian authors and organizations identified in the last two decades. This research should provide useful insights into the research hotspots of India in the present, past, and future.

A novel approach for DDoS attacks detection in COVID-19 scenario for small entrepreneurs.

The current COVID-19 issue has altered the way of doing business. Now that most customers prefer to do business online, many companies are shifting their business models, which attracts cyber attackers to launch several kinds of cyberattacks against commercial companies simultaneously. The most common and lethal DDoS attack disables the victim's online resources. While large businesses can afford defensive measures against DDoS assaults, the situation is different for new entrepreneurs. Their lack of security resources restricts their ability to ward off DDoS attacks. Here, we aim to highlight the problems that prospective entrepreneurs should be aware of before joining the business, followed by a filtering mechanism that efficiently identifies DDoS assaults in the COVID-19 scenario, which is the subject of our research. The suggested approach employs statistical and machine learning techniques to discriminate between DDoS attack data and regular communication. Our suggested framework is cost-effective and identifies DDoS attack traffic with a 92.8% accuracy rate.

Privacy protection framework for face recognition in edge-based Internet of Things.

Edge computing (EC) gets the Internet of Things (IoT)-based face recognition systems out of trouble caused by limited storage and computing resources of local or mobile terminals. However, data privacy leak remains a concerning problem. Previous studies only focused on some stages of face data processing, while this study focuses on the privacy protection of face data throughout its entire life cycle. Therefore, we propose a general privacy protection framework for edge-based face recognition (EFR) systems. To protect the privacy of face images and training models transmitted between edges and the remote cloud, we design a local differential privacy (LDP) algorithm based on the proportion difference of feature information. In addition, we also introduced identity authentication and hash technology to ensure the legitimacy of the terminal device and the integrity of the face image in the data acquisition phase. Theoretical analysis proves the rationality and feasibility of the scheme. Compared with the non-privacy protection situation and the equal privacy budget allocation method, our method achieves the best balance between availability and privacy protection in the numerical experiment.

Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform.

Global warming is a leading world issue driving the common social objective of reducing carbon emissions. People have witnessed the melting of ice and abrupt changes in climate. Reducing electricity usage is one possible method of slowing these changes. In recent decades, there have been massive worldwide rollouts of smart meters that automatically capture the total electricity usage of houses and buildings. Electricity load disaggregation (ELD) helps to break down total electricity usage into that of individual appliances. Studies have implemented ELD models based on various artificial intelligence techniques using a single ELD dataset. In this paper, a powerline noise transformation approach based on optimized complete ensemble empirical model decomposition and wavelet packet transform (OCEEMD-WPT) is proposed to merge the ELD datasets. The practical implications are that the method increases the size of training datasets and provides mutual benefits when utilizing datasets collected from other sources (especially from different countries). To reveal the effectiveness of the proposed method, it was compared with CEEMD-WPT (fixed controlled coefficients), standalone CEEMD, standalone WPT, and other existing works. The results show that the proposed approach improves the signal-to-noise ratio (SNR) significantly.

Extended-Range Prediction Model Using NSGA-III Optimized RNN-GRU-LSTM for Driver Stress and Drowsiness.

Road traffic accidents have been listed in the top 10 global causes of death for many decades. Traditional measures such as education and legislation have contributed to limited improvements in terms of reducing accidents due to people driving in undesirable statuses, such as when suffering from stress or drowsiness. Attention is drawn to predicting drivers' future status so that precautions can be taken in advance as effective preventative measures. Common prediction algorithms include recurrent neural networks (RNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) networks. To benefit from the advantages of each algorithm, nondominated sorting genetic algorithm-III (NSGA-III) can be applied to merge the three algorithms. This is named NSGA-III-optimized RNN-GRU-LSTM. An analysis can be made to compare the proposed prediction algorithm with the individual RNN, GRU, and LSTM algorithms. Our proposed model improves the overall accuracy by 11.2-13.6% and 10.2-12.2% in driver stress prediction and driver drowsiness prediction, respectively. Likewise, it improves the overall accuracy by 6.9-12.7% and 6.9-8.9%, respectively, compared with boosting learning with multiple RNNs, multiple GRUs, and multiple LSTMs algorithms. Compared with existing works, this proposal offers to enhance performance by taking some key factors into account-namely, using a real-world driving dataset, a greater sample size, hybrid algorithms, and cross-validation. Future research directions have been suggested for further exploration and performance enhancement.

A novel analysis of COVID 19 risk in India incorporating climatic and socioeconomic Factors.

This study investigates the influence of climate variables (pressure, relative humidity, temperature and wind speed) in inducing risk due to COVID 19 at rural, urban and total (rural and urban) population scale in 623 pandemic affected districts of India incorporating the socioeconomic vulnerability factors. We employed nonstationary extreme value analysis to model the different quantiles of cumulative COVID 19 cases in the districts by using climatic factors as covariates. Wind speed was the most dominating climatic factor followed by relative humidity, pressure, and temperature in the evolution of the cases. The results reveal that stationarity, i.e., the COVID 19 cases which are independent of pressure, relative humidity, temperature and wind speed, existed only in 148 (23.7%) out of 623 districts. Whereas, strong nonstationarity, i.e., climate dependence, was detected in the cases of 474 (76.08%) districts. 334 (53.6%), 200 (32.1%) and 336 (53.9%) districts out of 623 districts were at high risk (or above) at rural, urban and total population scales respectively. 19 out of 35 states were observed to be under high (or above) Kerala, Maharashtra, Goa and Delhi being the most risked ones. The study provides high-risk maps of COVID 19 pandemic at the district level and is aimed at supporting the decision-makers to identify climatic and socioeconomic factors in augmenting the risks.

Impacts of biological globalization in the Mediterranean: unveiling the deep history of human-mediated gamebird dispersal.

Humans have a long history of moving wildlife that over time has resulted in unprecedented biotic homogenization. It is, as a result, often unclear whether certain taxa are native to a region or naturalized, and how the history of human involvement in species dispersal has shaped present-day biodiversity. Although currently an eastern Palaearctic galliform, the black francolin (Francolinus francolinus) was known to occur in the western Mediterranean from at least the time of Pliny the Elder, if not earlier. During Medieval times and the Renaissance, the black francolin was a courtly gamebird prized not only for its flavor, but also its curative, and even aphrodisiac qualities. There is uncertainty, however, whether this important gamebird was native or introduced to the region and, if the latter, what the source of introduction into the western Mediterranean was. Here we combine historical documentation with a DNA investigation of modern birds and archival (13th-20th century) specimens from across the species' current and historically documented range. Our study proves the black francolin was nonnative to the western Mediterranean, and we document its introduction from the east via several trade routes, some reaching as far as South Asia. This finding provides insight into the reach and scope of long-distance trade routes that serviced the demand of European aristocracy for exotic species as symbols of wealth and prestige, and helps to demonstrate the lasting impact of human-mediated long-distance species dispersal on current day biodiversity.

Human-wildlife conflict: proximate predictors of aggression between humans and rhesus macaques in India.

Macaques live in close contact with humans across South and Southeast Asia, and direct interaction is frequent. Aggressive contact is a concern in many locations, particularly among populations of rhesus and longtail macaques that co-inhabit urbanized cities and towns with humans. We investigated the proximate factors influencing the occurrence of macaque aggression toward humans as well as human aggression toward macaques to determine the extent to which human behavior elicits macaque aggression and vice versa. We conducted a 3-month study of four free-ranging populations of rhesus macaques in Dehradun, India from October-December 2012, using event sampling to record all instances of human-macaque interaction (N = 3120). Our results show that while human aggression was predicted by the potential for economic losses or damage, macaque aggression was influenced by aggressive or intimidating behavior by humans as well as recent rates of conspecific aggression. Further, adult female macaques participated in aggression more frequently than expected, whereas adult and subadult males participated as frequently as expected. Our analyses demonstrate that neither human nor macaque aggression is unprovoked. Rather, both humans and macaques are responding to one another's behavior. Mitigation of human-primate conflict, and indeed other types of human-wildlife conflict in such coupled systems, will require a holistic investigation of the ways in which each participant is responding to, and consequently altering, the behavior of the other.

Liver Transplantation Research from India During the Last 25 Years: 1998-2022.

Background and aims: Liver transplant surgery has been performed in India for the last 25 years. We aimed to analyse the trends, characteristics, and key elements in the field of liver transplantation research from India. Methods: On April 23, 2023, we conducted a search of the Scopus database for the literature on liver transplantation research, using a well-defined search strategy. MS Excel and VOS viewer software programs were used to examine the articles for organisation, author, journal, keywords, and high-cited literature. Results: This analysis examined a total of 556 papers, which constituted only a 1.55% share of the global output. These papers involved 442 organizations, 1575 authors, and 147 journals. External funding was received in 4.13% and 23.56% were involved in international collaboration. Three Delhi-NCR organizations, namely the Medanta-The Medicity (n = 63), Institute of Liver & Biliary Sciences (n = 60), and Indraprastha Apollo Hospital (n = 48) led in publication productivity. M. Rela (n = 90) and A.S. Soin (n = 63) were the leading authors in publication productivity, while S. Sudhindran and P. Bhangui were the most impactful authors. Liver Transplantation (n = 96) and Journal of Clinical & Experimental Hepatology (n = 65) published the maximum number of these papers, whereas, Annals of Surgery and Journal of Hepatology led in the citation impact per paper. The most significant keywords were "Liver Transplantation" (n = 484), and "Living Donor" (n = 254). Only 1.80% (n = 10) of the papers were highly cited papers that received 50 to 142 citations and they together registered 69.9 citations per paper. Conclusion: Although the number of publications on liver transplantation from India started growing recently, it forms only 1.55% of the global report. There is an unmet need to increase government-supported research and multicenter collaborative studies at national and international levels for high-quality patient care.

Global Stem Cell Research in Orthopaedics: A Bibliometric Study from 1995 to 2020.

Background: The research field of stem cell-based therapies in orthopaedics has witnessed significant growth in the recent past. We aimed to identify and analyze the bibliometric characteristics of the global highly cited papers (HCPs) in stem cell research in orthopaedics. Methods: This study relied on secondary data extracted from Scopus, Elsevier's abstract and citation database. An advanced search string was employed, for the period from 1995 to 2020. For each paper, the extracted information included the number of citations, title, authors (name, number, authorship position, and country), year of publication, title of the journals, study design, and thematic field. The VOSviewer (1.6.20) was used to uncover relationships between authors, institutions, keywords, and publications. Results: There were a total of 1427 publications and out of these 186 papers had 100 or more citations (range 100-2644) and were considered as HCPs. The average citation per paper (CPP) was 265.8. Only 4% of the top HCPs contributed 20% of the total citations of all HCPs. All the HCPs were published from high-income countries, and the USA was the leading country in all aspects of publication on stem cell research. Méndez-Ferrer S registered the highest citation (n = 2644), Prockop DJ was the most prolific author (n = 8 papers), and Harvard Medical School, USA emerged as the most prolific organization with 12 HCPs. Conclusion: Global research in stem cell therapies for orthopaedic problems is making strides, and is an emerging field of research. Stem cell research offers the potential for improved treatment outcomes for various musculoskeletal conditions. Supplementary Information: The online version contains supplementary material available at 10.1007/s43465-024-01160-0.

Global research output and highly-cited publications on prosthetic joint infections: A bibliometric analysis (2003-2022).

Backgroundand aims: Prosthetic Joint Infection (PJI) is a serious clinical problem after Arthroplasty. The research field on PJI is emerging, but there is a paucity of information on the most impactful publications on it. This prompted us to conduct a bibliometric analysis of the global research output, from 2003 to 2022, to identify the growth of publications, the key players in this research field and to evaluate the characteristics of highly-cited publications (HCPs) on the PJI. Methods: Publications related to PJI research were identified globally from the Scopus database, using specific keywords, covering the literature from 2003 to 2022. The HCPs were considered those with 100 or more citations. Information on publication year, citation count, funding sources, title, author, journal, country, institution, research area, and strategic keywords were collected from these HCPs. Publication data was imported into Microsoft Excel and analyzed further using VOSviewer and R software. Results: There were 182 HCPs (3.12%), which received a total citation of 124701 (average CPP of 21.41), with the citation range from 100 to 1921. Research articles were the most predominant publications (69.2%), but their average citations per paper (CPP) of 189.78 was lower than that of Review articles (average CPP: 253.17). The USA has been the leading country in terms of total publications (31.58%), and HCPs (36.99%), followed by Switzerland, Spain, UK and China. There were no HCPs from developing countries. J. Parvizi of Thomas Jefferson University, USA (with a total publications of 31 and an average CPP of 315.7), and W. Zimmerli of Basel University, Switzerland (with a TP of 11 and an average CPP of 341.9), were the most productive and impactful authors in PJI global research output. Conclusion: This bibliometric analysis identified the most productive and impactful authors, organizations, countries, and journals in the research of PJI, of the last two decades.

Global Research on Osteoarthritis During 1994-2023: A Scientometric Assessment of Publications and Citations.

Introduction: This study presents a global research scenario in the broad domain of osteoarthritis (OA) research, using quantitative and qualitative publication and citation indicators. Methods: The study is based on 45,368 global publications, sourced from the Scopus bibliographical database, covering three decades (1994-2023). We studied the performance of the top 12 developed and top 12 developing countries. The key countries, organizations and authors at national and international levels were identified. The broad subject areas and key journals contributing to global OA research were delineated, besides identifying the broad characteristics of highly cited papers in the field. Results: The United States and China were the most productive countries, while the Netherlands and Canada made the largest citation impact. Harvard Medical School and the University of Sydney made the most contribution, while Boston University and Pfizer Inc., USA registered the highest citation impact. Hunter DJ and Guermazi A were the most productive authors, while Lohmander LS, and Hochberg MC registered the highest citation impact. Osteoarthritis and Cartilage (n = 4879) and Annals of the Rheumatic Diseases (n = 786) published the maximum papers, while Arthritis and Rheumatism and Nature Reviews Rheumatology registered the largest citation impact. The highly cited papers with 100 or more citations constituted 6.25% of the total publications. Conclusions: There has been a systematic growth of publications on OA. The research on OA was mainly done in developed countries, with the maximum publications coming from the United States of America, China and Canada. The most impactful publications on OA were from the Netherlands, Canada and the United States of America. Supplementary Information: The online version contains supplementary material available at 10.1007/s43465-024-01111-9.

Research on Non-alcoholic Fatty Liver Disease From Indian Subcontinent: A Bibliometric Analysis of Publications During 2001-2022.

Introduction: The non-alcoholic fatty liver disease (NAFLD) is common in the Indian Subcontinent. We aimed to examine the bibliometric characteristics of the publications arising from the countries of the Indian Subcontinent on NAFLD, over the last two decades. Methods: Publications on NAFLD from Indian Subcontinent during the period of 2001-2022 were retrieved from the Scopus database. Various important bibliometric parameters were studied from the retrieved publications and were exported to MS-Excel for analysis. VOSviewer software was used for analyzing co-author collaborative networks and keyword co-occurrence networks. Results: There is a rising trend of publications, especially in the last decade, with an average annual growth of 28.95% and an absolute growth of 526.21% between 2013 and 2022, compared to 2001-2012. From Indian Subcontinent's authors, 1053 papers were indexed in Scopus, with the majority (81.3%) being from India. Indian Subcontinent holds 13th rank globally with 3.43% share of global output. External funding was received for 15.76% publications and 24.59% papers were prepared with international collaboration, and these received much higher citations per paper. Research output is low, only 3.43% of global share. Regional research cooperation among countries of Indian subcontinent is also poor. Further, only 3.61% of papers were highly cited. Conclusion: Despite a high prevalence of NAFLD in Indian Subcontinent, the research output is low and of low impact. Further, the research collaboration between these Indian Subcontinent needs improvement.

Certificateless Public Auditing Scheme With Data Privacy and Dynamics in Group User Model of Cloud-Assisted Medical WSNs.

With the application of wireless sensor network (WSN) in healthcare field, online sharing of medical data has attracted more and more attention. However, wearable sensor nodes are limited in energy, storage space and data processing capacity, which largely restricts their deployment in resource demand application scenarios. Fortunately, cloud storage services can enrich the capabilities of wearable sensors and provide an effective method for people to share data within a group. However, as medical data directly relates to patients' health and privacy information, ensuring the integrity and privacy of medical records stored in cloud servers becomes a key issue to be urgently solved. Many public data auditing schemes have been put forward to address the above issues. Unfortunately, most of them have security vulnerabilities or poor functionality and performance. In this paper, we come up with a secure and efficient certificateless public auditing scheme for cloud-assisted medical WSNs, which not only supports dynamic data sharingand privacy protection, but also achieves efficient group user revocation. Security analysis and performance evaluation demonstrate that our scheme significantly reduce the total computation cost while achieving a higher security level. Compared with other related schemes, our new proposal is more suitable for group user data sharing in cloud-assisted medical WSNs.

Hierarchical Sampling for the Visualization of Large Scale-Free Graphs.

Graph sampling frequently compresses a large graph into a limited screen space. This paper proposes a hierarchical structure model that partitions scale-free graphs into three blocks: the core, which captures the underlying community structure, the vertical graph, which represents minority structures that are important in visual analysis, and the periphery, which describes the connection structure between low-degree nodes. A new algorithm named hierarchical structure sampling (HSS) was then designed to preserve the characteristics of the three blocks, including complete replication of the connection relationship between high-degree nodes in the core, joint node/degree distribution between high- and low-degree nodes in the vertical graph, and proportional replication of the connection relationship between low-degree nodes in the periphery. Finally, the importance of some global statistical properties in visualization was analyzed. Both the global statistical properties and local visual features were used to evaluate the proposed algorithm, which verify that the algorithm can be applied to sample scale-free graphs with hundreds to one million nodes from a visualization perspective.