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Machining quality prediction is the critical link of quality control in parts machining. With the advent of the Industry 4.0 era, intelligent manufacturing and data-driven technologies bring new ideas for quality control in complex machining processes. Quality control is complicated for multi-process, multi-condition, small-batch, and high-precision parts processing requirements. To solve this problem, this paper proposes a machining quality prediction method based on the machining error transfer network and the grey neural network. Initially, by constructing a processing error transfer network, the error transfer law in part processing is described, and the PageRank algorithm and the influence degree of the nodes are used to determine the critical quality features. Additionally, the problem of low prediction accuracy due to small sample data and multiple coupling relationships is solved using the grey neural network algorithm, and a high accuracy prediction of critical quality features is achieved. Finally, the effectiveness and reliability of the method are verified by the case of medium-speed marine diesel engine fuselage processing. The results indicate that this method not only effectively identifies critical quality features in the machining process of complex parts, but it also maintains a high predictive accuracy for these features, even with small samples and limited data. [ABSTRACT FROM AUTHOR]

The construction industry faces significant challenges with frequent accidents, largely due to the inefficient use of safety guidelines. These guidelines, which are often text and figure heavy, demand substantial human effort to identify the most relevant items for specific tasks and conditions. Additionally, the guidelines contain both central and peripheral elements, and central items are critical yet difficult to identify without extensive domain knowledge. This study proposes a novel recommendation framework to enhance the usability of these safety guidelines. By leveraging natural language processing (NLP) and knowledge graph (KG) modeling techniques, unstructured safety texts are transformed into a structured, interconnected KG. The PageRank and Louvain Clustering algorithm is then employed to rank guidelines by their relevance and importance. A case study on "High-rise Building Construction (General) Safety and Health Guidelines", using 'scaffolding' as the keyword, demonstrates the framework's effectiveness in improving retrieval efficiency and practical application. The analysis highlighted key clusters such as 'fall', 'drop', and 'scaffolding', with critical safety measures identified through their interconnections. This research not only overcomes the fragmentation of safety management documents but also contributes to advancing hazard analysis and risk prevention practices in construction management. [ABSTRACT FROM AUTHOR]

Rainstorm disasters cause serious threats to people's lives and property. Enhancing emergency response and decision-making capabilities for rainstorm disasters is necessary. In this paper, 87 rainstorm disasters worldwide were first analysed, and secondary events across 15 urban lifeline systems were summarized. Based on the obtained findings and the characteristics of rainstorm disaster evolution and complex network theory, a model of rainstorm disaster chains in urban lifeline systems was constructed, and both partial and overall analyses of this model were performed. With the use of the PageRank risk matrix method, quantitative node risk levels were calculated for different parts of the model, and complex network theory was applied to assess the overall chain risk. The results showed that the highest risk disaster chain was flood → houses submerged or collapsed → road damaged → traffic congestion or paralysis. The most important node was traffic congestion or paralysis, underlining the acute need for emergency response measures in urban traffic systems during rainstorm disasters. Overall, this research provides a crucial direction for preventing rainstorm disasters in urban lifeline systems. [ABSTRACT FROM AUTHOR]

Research on graph-based automatic text summarization for Arabic, the official language of 26 nations with over 200 million speakers, as well as other prevalent languages, has recently increased due to the ability of these approaches to handle linguistic peculiarities such as complex morphological linkages. The present paper proposes a graph-based extractive Arabic text summarization (GEATS) technique that employs word embedding and PageRank algorithms for feature extraction and sentence ordering. The efficiency of the GEATS approach versus the state-of-the-art methods is analyzed based on the quality of the produced summaries over the F-measure values. The findings indicated that it outperformed the nearest alternative by an advantage of over 7.5%. [ABSTRACT FROM AUTHOR]

To effectively promote the efficient dissemination of sci-tech journals and improve the influence of sci-tech journals, a precise push method of sci-tech journals based on knowledge graph reasoning is proposed, using knowledge graph to build network to realize push reasoning of scientific and Technological Journals. Based on the one-way author relationship network diagram and the two-way keyword network diagram, the PageRank algorithm is used to calculate the weight of network vertices, quantitatively and accurately identify the research direction of push customers and predict customers, and realize the accurate push management of content, so as to obtain the basis for the construction of network diagram. Based on the previous article in the Journal of the information society of science and technology of China, this paper constructs a knowledge reasoning diagram, mines 52 co-author cases and 60 related papers as experimental data, and verifies the progressiveness method through customer value. According to the network diagram, the progressiveness of this method is verified. The experimental results show that the knowledge map network reasoning method proposed in this paper has high accuracy in identifying the push objects of scientific and technological journals and accurately predicting the research direction. The matching degree between the number of customers and the pushed content. Using knowledge map network can ensure the timeliness and accuracy of pushing scientific and technological journals. [ABSTRACT FROM AUTHOR]

Aiming at the problems of poor accuracy and narrow applicability of the existing key acupoint mining algorithms, a high specificity acupoint mining algorithm based on a 3‐node motif is proposed by introducing higher‐order interactions between multiple acupoints in the acupoint‐disease network. Comparing this algorithm with five other acupoint importance assessment algorithms in terms of resolution, network loss, and accuracy, the results show that the key acupoints identified by this algorithm have obvious destructive effects on the connectivity of the network, which indicates that the key acupoints are the core of the topology of acupoint‐disease network and have high synergistic co‑operation with other acupoints. The stability of this algorithm ensures the reliability of the key acupoints. From the perspective of the network topology and the high synergy between acupoints, the key acupoints found by the algorithm can be used as the core acupoints in the acupoint network, helping researchers to explore targeted and highly effective combinations of acupoints. [ABSTRACT FROM AUTHOR]

Domestic and international risk shocks have greatly increased the demand for systemic risk management in China. This paper estimates China's multi-layer financial network based on multiple financial relationships among banks, assets, and firms, using China's banking system data in 2021. An improved PageRank algorithm is proposed to identify systemically important banks and other economic sectors, and a stress test is conducted. This study finds that China's multi-layer financial network is sparse, and the distribution of transactions across financial markets is uneven. Regulatory authorities should support economic recovery and adjust the money supply, while banks should differentiate competition and manage risks better. Based on the PageRank index, this paper assesses the systemic importance of large commercial banks from the perspective of network structure, emphasizing the role of banks' transaction behavior and market participation. Different industries and asset classes are also assessed, suggesting that increased attention should be paid to industry risks and regulatory oversight of bank investments. Finally, stress tests confirm that the improved PageRank algorithm is applicable within the multi-layer financial network, reinforcing the need for prudential supervision of the banking system and revealing that the degree of transaction concentration will affect the systemic importance of financial institutions. [ABSTRACT FROM AUTHOR]

The modeling of Computer Numerical Control (CNC) lathes has been constrained by various issues, such as the use of single methods, inaccurate model parameters, and inadequate consideration of subsystem failures. To overcome these challenges, this study proposes a reliability modeling method based on the Pagerank Algorithm for analyzing failure correlations in CNC lathe subsystems. The proposed method involves analyzing the failure correlation between subsystems through failure transfer-directed graph and representing the relationship between subsystems using an adjacency matrix. Based on the fault correlation between subsystem, a reliability model of the CNC lathe subsystem is established, and the Pagerank Algorithm is utilized to optimize the influence degree of each subsystem. To validate the proposed method, field use failure information of CNC lathe is utilized as an example, and the above theoretical modeling is verified and analyzed. Finally, the reliability model based on failure correlation is verified using the D-test value, relative error method, and accurate factor. In comparison to the reliability model without considering failure correlation, the proposed model results in a small relative error and more accurate value. [ABSTRACT FROM AUTHOR]

Abstract This paper develops an improved structural health assessment method for cable-stayed bridge to address the issue of neglecting component correlations in existing assessment standards. Firstly, the directed graph of fault transmission between components in the cable-stayed bridge system was constructed. The Pagerank algorithm was used to analyze the degree of correlation between these components, and then the influencing degree of and the influenced degree of each component were determined. Secondly, considering the failure rate of individual components and the influenced degree of other component faults, a condition evaluation method with component correlation for cable-stayed bridge was proposed. Finally, the improved assessment method was applied to a super large-span steel cable-stayed bridge as a case study and compared with the relevant assessment specifications. The results show that main girder alignment, cable force and main tower alignment have a greater degree of correlation with other components and are important indicators for bridge health monitoring. Visual inspection of main girder and bridge bearing are the fault appearance components and should be paid attention to in preventive maintenance. The drainage system and electromechanical facilities are the fault source components and must be kept in good condition in daily inspections. The proposed method considers the interrelationships among components more comprehensively and can provide more reliable bridge health assessment results to support bridge maintenance decisions.

The construction industry faces significant challenges with frequent accidents, largely due to the inefficient use of safety guidelines. These guidelines, which are often text and figure heavy, demand substantial human effort to identify the most relevant items for specific tasks and conditions. Additionally, the guidelines contain both central and peripheral elements, and central items are critical yet difficult to identify without extensive domain knowledge. This study proposes a novel recommendation framework to enhance the usability of these safety guidelines. By leveraging natural language processing (NLP) and knowledge graph (KG) modeling techniques, unstructured safety texts are transformed into a structured, interconnected KG. The PageRank and Louvain Clustering algorithm is then employed to rank guidelines by their relevance and importance. A case study on “High-rise Building Construction (General) Safety and Health Guidelines”, using ‘scaffolding’ as the keyword, demonstrates the framework’s effectiveness in improving retrieval efficiency and practical application. The analysis highlighted key clusters such as ‘fall’, ‘drop’, and ‘scaffolding’, with critical safety measures identified through their interconnections. This research not only overcomes the fragmentation of safety management documents but also contributes to advancing hazard analysis and risk prevention practices in construction management.

Machining quality prediction is the critical link of quality control in parts machining. With the advent of the Industry 4.0 era, intelligent manufacturing and data-driven technologies bring new ideas for quality control in complex machining processes. Quality control is complicated for multi-process, multi-condition, small-batch, and high-precision parts processing requirements. To solve this problem, this paper proposes a machining quality prediction method based on the machining error transfer network and the grey neural network. Initially, by constructing a processing error transfer network, the error transfer law in part processing is described, and the PageRank algorithm and the influence degree of the nodes are used to determine the critical quality features. Additionally, the problem of low prediction accuracy due to small sample data and multiple coupling relationships is solved using the grey neural network algorithm, and a high accuracy prediction of critical quality features is achieved. Finally, the effectiveness and reliability of the method are verified by the case of medium-speed marine diesel engine fuselage processing. The results indicate that this method not only effectively identifies critical quality features in the machining process of complex parts, but it also maintains a high predictive accuracy for these features, even with small samples and limited data.

In the era of the Internet and big data, the PageRank (PR) algorithm is a constantly evolving research field. However, there is no systematic research to explore the overall development trend of the PR domain. This article evaluates 1446 articles related to the PR algorithm and provides a thorough understanding of the PR field through the main path analysis (MPA). Through two basic main paths, a number of papers that play a leading role have been identified, which outline the backbone of the PR domain. Based on the analysis of multiple main paths, four main subareas have been investigated. There are accelerating the computation of PR, comprehensive applications of PR, researches on academic impact assessment and age preference in network evolution. Finally, this article discusses the research findings and the future directions of the PR field. It is the first attempt to identify the development trend of the PR domain through MPA, thus providing an insight into the knowledge evolution of the PR field over the past two decades. [ABSTRACT FROM AUTHOR]

This paper develops an improved structural health assessment method for cable-stayed bridge to address the issue of neglecting component correlations in existing assessment standards. Firstly, the directed graph of fault transmission between components in the cable-stayed bridge system was constructed. The Pagerank algorithm was used to analyze the degree of correlation between these components, and then the influencing degree of and the influenced degree of each component were determined. Secondly, considering the failure rate of individual components and the influenced degree of other component faults, a condition evaluation method with component correlation for cable-stayed bridge was proposed. Finally, the improved assessment method was applied to a super large-span steel cable-stayed bridge as a case study and compared with the relevant assessment specifications. The results show that main girder alignment, cable force and main tower alignment have a greater degree of correlation with other components and are important indicators for bridge health monitoring. Visual inspection of main girder and bridge bearing are the fault appearance components and should be paid attention to in preventive maintenance. The drainage system and electromechanical facilities are the fault source components and must be kept in good condition in daily inspections. The proposed method considers the interrelationships among components more comprehensively and can provide more reliable bridge health assessment results to support bridge maintenance decisions. [ABSTRACT FROM AUTHOR]

The construction of information resources based on the development needs of disciplines is the fundamental guarantee for carrying out disciplinary service work. An English teaching resource guarantee system is created by combining the OAI protocol with Open URL technology in this paper. Furthermore, the VIPS algorithm is used for style tree construction, combined with split bar weights and mixed text density for extracting web page body information. Then, the weight factor is introduced to improve the PageRank algorithm, and the critical resource selection strategy is combined to achieve the mining and classification of English teaching resources. In addition, the study introduces a knowledge graph for entity recognition and combines graph convolutional networks with user preference weights for optimizing teaching resource recommendations. Based on this basis, the performance of the constructed English teaching resources assurance system is verified for webpage information extraction, teaching resources classification, and personalized recommendations. It is found that the correct rate of English webpage information extraction is 96.42% when the text density of the preceding and following text is 0.334 and 0.527, respectively, and the personalized recommendation performance of English teaching resources is 4.12% higher than the best-performing KGCN model in terms of AUC index. The English teaching resources guarantee system can effectively ensure the precise classification of teaching resources in the English language and provide new guidance for furthering the automation of English teaching resources.

A novel non-centralised dispatch strategy is presented for wake redirection to optimise large-scale offshore wind farms operation, creating a balanced control between power production and fatigue thrust loads evenly among the wind turbines. This approach is founded on a graph sparsification partitioning strategy that takes into account the impact of wake propagation. More specifically, the breadth-first search algorithm is employed to identify the subgraph based on the connectivity of the wake direction graph, while the PageRank centrality computation algorithm is utilised to determine and rank scores for the shared turbines' affiliation with the subgraphs. By doing so, the wind farm is divided into smaller subsets of partitioned turbines, resulting in decoupling. The objective function is then formulated by incorporating penalty terms, specifically the standard deviation of fatigue thrust loads, into the maximum power equation. Meanwhile, the non-centralisation sequential quadratic programming optimisation algorithm is subsequently employed within each partition to determine the control actions while considering the objectives of the respective controllers. Finally, the simulation results of case studies prove to reduce computational costs and improve wind farm power production by balancing accumulated fatigue thrust loads over the operational lifetime as much as possible. [ABSTRACT FROM AUTHOR]

Consensus algorithms are the core technology of a blockchain and directly affect the implementation and application of blockchain systems. Delegated proof of stake (DPoS) significantly reduces the time required for transaction verification by selecting representative nodes to generate blocks, and it has become a mainstream consensus algorithm. However, existing DPoS algorithms have issues such as "one ballot, one vote", a low degree of decentralization, and nodes performing malicious actions. To address these problems, an improved DPoS algorithm based on community discovery is designed, called CD-DPoS. First, we introduce the PageRank algorithm to improve the voting mechanism, achieving "one ballot, multiple votes", and we obtain the reputation value of each node. Second, we propose a node voting enthusiasm measurement method based on the GN algorithm. Finally, we design a comprehensive election mechanism combining node reputation values and voting enthusiasm to select secure and reliable accounting nodes. A node credit incentive mechanism is also designed to effectively motivate normal nodes and drive out malicious nodes. The experimental simulation results show that our proposed algorithm has better decentralization, malicious node eviction capabilities and higher throughput than similar methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South-Central Minzu University (Natural Science Edition) is the property of Journal of South-Central Minzu University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Since the existing software defined network (SDN) security prediction methods do not consider the attack cost and the impact of controller vulnerabilities on SDN security, a Bayesian attack graph-based algorithm to assessing SDN intrusion intent was proposed.The PageRank algorithm was used to obtain the criticality of the device, and combining with the vulnerability value, attack cost, attack benefit and attack preference, an attack graph was constructed, and a risk assessment model was established to predict the intrusion path.Through experimental comparison, it is obvious that the proposed model can more accurately predict the intrusion path, effectively ensure the accuracy of security prediction, and provide a basis for SDN defense.

Domestic and international risk shocks have greatly increased the demand for systemic risk management in China. This paper estimates China’s multi-layer financial network based on multiple financial relationships among banks, assets, and firms, using China’s banking system data in 2021. An improved PageRank algorithm is proposed to identify systemically important banks and other economic sectors, and a stress test is conducted. This study finds that China’s multi-layer financial network is sparse, and the distribution of transactions across financial markets is uneven. Regulatory authorities should support economic recovery and adjust the money supply, while banks should differentiate competition and manage risks better. Based on the PageRank index, this paper assesses the systemic importance of large commercial banks from the perspective of network structure, emphasizing the role of banks’ transaction behavior and market participation. Different industries and asset classes are also assessed, suggesting that increased attention should be paid to industry risks and regulatory oversight of bank investments. Finally, stress tests confirm that the improved PageRank algorithm is applicable within the multi-layer financial network, reinforcing the need for prudential supervision of the banking system and revealing that the degree of transaction concentration will affect the systemic importance of financial institutions.

Scholars have conducted research on amendable ledger technology in response to the problems such as irregular data and misuse of uploaded data in the blockchain ledger. This paper proposed a blockchain ledger amendment scheme based on supervisor groups to improve the traditional scheme. The scheme addressed the problems of low modification efficiency and malicious nodes messing up security in traditional correction schemes. The scheme filtered out the supervisor group member nodes through an improved PageRank-based blockchain node credit ranking algorithm, which in turn to improve the efficiency of the ledger revision process under trustworthy premises. The scheme designed an improved random selection of reviser algorithm and the use of verifiable random functions to construct a node verification recourse mechanism. The mechanism ensures the traceability and security of the ledger data revision process. The analysis and simulation results show that the scheme has high security and outperforms traditional methods in terms of processing efficiency and resource utilisation. [ABSTRACT FROM AUTHOR]

With the increasing importance of network security, more attention is paid to the research on network security situation awareness, while network security situation assessment is the core of situation awareness. The existing situation assessment technology has many problems, such as unitary methodology, large-scale subjective factors and so on, affecting the accuracy of situation assessment. Combined with the traditional network security technology, this paper sets up a three-layer evaluation index system, and puts forward an overall network security situation evaluation model based on hidden Markov model, PageRank algorithm and D-S evidence theory, through which, the overall situation is evaluated after using different fusion algorithms in different fusion stages. The simulation result shows that the above assessment method is more accurate and efficient than the method using single model.

The existence of related faults between components brings great difficulties to the analysis of the importance of system components. How to quantify the influence of related faults and evaluate the importance of components is one of the hot issues in current research. In this paper, under the assumption that the fault propagation obeys the Markov process, the PageRank algorithm is integrated into the decision-making trial and evaluation laboratory (DEMATEL). On the basis, the calculation of influencing degree and influenced degree between components is studied to quantify the influence of related faults, and the problem of subjective evaluation of weight coefficient in traditional DEMATEL is solved. The rationality is verified through the method of combining the Interpretative Structural Modeling Method (ISM) and direct relation matrix. The importance of system related faults is identified accurately based on the calculation of center degree and cause degree, and the central-related faults of CNC machine tools are analyzed as an example to verify the effectiveness of the proposed method.

The types and functions of social networking sites are becoming more abundant with the prevalence of self-media culture, and the number of daily active users of social networking sites represented by Weibo and Zhihu continues to expand. There are key node users in social networks. Compared with ordinary users, their influence is greater, their radiation range is wider, and their information transmission capabilities are better. The key node users playimportant roles in public opinion monitoring and hot event prediction when evaluating the criticality of nodes in social networking sites. In order to solve the problems of incomplete evaluation factors, poor recognition rate and low accuracy of key nodes of social networking sites, this paper establishes a social networking site key node recognition algorithm (SNSKNIS) based on PageRank (PR) algorithm, and evaluates the importance of social networking site nodes in combination with the influence of nodes and the structure of nodes in social networks. This article takes the Sina Weibo platform as an example, uses the key node identification algorithm system of social networking sites to discover the key nodes in the social network, analyzes its importance in the social network, and displays it visually. [ABSTRACT FROM AUTHOR]

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The task of calculating the productivity of collective scientific subjects is a relevant issue in scientometrics. This study formalized the problem of assessing productivity trends of collective scientific subjects. The TWPR-CI method for calculating the performance based on the modified PageRank algorithm is described. Formulas for calculating productivity have been derived that make it possible to take into account a change in the productivity of collective scientific subjects over time. The indicators of the basic average absolute change in performance and the chain average relative change in performance were chosen as the basis. To select promising, from the point of view of scientific work, collective subjects, preference is given to those whose basic average absolute change in productivity is positive or the chain average relative change in productivity exceeds unity. Verification of the method for assessing performance trends of collective scientific entities based on the modified PageRank algorithm using the public dataset Citation Network Dataset was carried out. The dataset includes more than 5 million scientific publications and 48 million citations. The citation of scientific publications of 27,500 collective scientific subjects for the period from 2000 to 2022 was analyzed. For this period, for 15 selected collective scientific subjects, performance is calculated using the TWPR-CI method, as well as estimates of productivity trends based on their average relative change. There are three classes of collective scientific subjects according to productivity trends. The results indicate the relevance of the proposed method for quantifying the productivity trends of collective scientific entities (higher education institutions, scientific institutes, laboratories, and other institutions engaged in scientific activities). [ABSTRACT FROM AUTHOR]

A novel non-centralised dispatch strategy is presented for wake redirection to optimise large-scale offshore wind farms operation, creating a balanced control between power production and fatigue thrust loads evenly among the wind turbines. This approach is founded on a graph sparsification partitioning strategy that takes into account the impact of wake propagation. More specifically, the breadth-first search algorithm is employed to identify the subgraph based on the connectivity of the wake direction graph, while the PageRank centrality computation algorithm is utilised to determine and rank scores for the shared turbines’ affiliation with the subgraphs. By doing so, the wind farm is divided into smaller subsets of partitioned turbines, resulting in decoupling. The objective function is then formulated by incorporating penalty terms, specifically the standard deviation of fatigue thrust loads, into the maximum power equation. Meanwhile, the non-centralisation sequential quadratic programming optimisation algorithm is subsequently employed within each partition to determine the control actions while considering the objectives of the respective controllers. Finally, the simulation results of case studies prove to reduce computational costs and improve wind farm power production by balancing accumulated fatigue thrust loads over the operational lifetime as much as possible.

Consensus algorithms are the core technology of a blockchain and directly affect the implementation and application of blockchain systems. Delegated proof of stake (DPoS) significantly reduces the time required for transaction verification by selecting representative nodes to generate blocks, and it has become a mainstream consensus algorithm. However, existing DPoS algorithms have issues such as “one ballot, one vote”, a low degree of decentralization, and nodes performing malicious actions. To address these problems, an improved DPoS algorithm based on community discovery is designed, called CD-DPoS. First, we introduce the PageRank algorithm to improve the voting mechanism, achieving “one ballot, multiple votes”, and we obtain the reputation value of each node. Second, we propose a node voting enthusiasm measurement method based on the GN algorithm. Finally, we design a comprehensive election mechanism combining node reputation values and voting enthusiasm to select secure and reliable accounting nodes. A node credit incentive mechanism is also designed to effectively motivate normal nodes and drive out malicious nodes. The experimental simulation results show that our proposed algorithm has better decentralization, malicious node eviction capabilities and higher throughput than similar methods.

Social networks have increasingly become important and popular in modern times. Moreover, the influence of social networks plays a vital role in various organizations, including government organizations, academic research organizations and corporate organizations. Therefore, strategizing the optimal propagation strategy in social networks has also become more important. Increasing the precision of evaluating the propagation probability of social networks can indirectly influence the investment of cost, manpower and time for information propagation to achieve the best return. This study proposes a new algorithm, which includes a scale-free network, Barabási–Albert model, binary-addition tree (BAT) algorithm, PageRank algorithm, Personalized PageRank algorithm and a new BAT algorithm to calculate the propagation probability of social networks. The results obtained after implementing the simulation experiment of social network models show that the studied model and the proposed algorithm provide an effective method to increase the efficiency of information propagation in social networks. In this way, the maximum propagation efficiency is achieved with the minimum investment. [ABSTRACT FROM AUTHOR]

Network pharmacology is a method to study the mechanism of a Traditional Chinese Medicine (TCM) prescription on a disease. However, most articles using network pharmacology to study the mechanism did not combine the weight information of herbs, the weight information of targets of disease, and the interaction information between targets together. We propose a method, network pharmacology combined with two iterations of PageRank algorithm, to make use of these information. It takes prescription-disease system as a whole, calculates PageRank score of targets in the prescription-disease system, which means an importance in the system, and the score is used to rank the analysis results of GO and KEGG pathway which help us to analyze the mechanism of a prescription on a disease. At last, we use two prescription-disease pairs which have been proved effectiveness in clinical trials: Qingfei Paidu Decoction on COVID-19, and FuFang DanShen Diwan on Coronary Heart Disease, and find that the results of our method are consistent with some results of clinical trials.

Background: Neuronal cell cycle re-entry (CCR) is a mechanism, along with amyloid-β (Aβ) oligomers and hyperphosphorylated tau proteins, contributing to toxicity in Alzheimer's disease (AD).Objective: This study aimed to examine the putative factors in CCR based on evidence corroboration by combining meta-analysis and co-expression analysis of omic data.Methods: The differentially expressed genes (DEGs) and CCR-related modules were obtained through the differential analysis and co-expression of transcriptomic data, respectively. Differentially expressed microRNAs (DEmiRNAs) were extracted from the differential miRNA expression studies. The dysregulations of DEGs and DEmiRNAs as binary outcomes were independently analyzed by meta-analysis based on a random-effects model. The CCR-related modules were mapped to human protein-protein interaction databases to construct a network. The importance score of each node within the network was determined by the PageRank algorithm, and nodes that fit the pre-defined criteria were treated as putative CCR-related factors.Results: The meta-analysis identified 18,261 DEGs and 36 DEmiRNAs, including genes in the ubiquitination proteasome system, mitochondrial homeostasis, and CCR, and miRNAs associated with AD pathologies. The co-expression analysis identified 156 CCR-related modules to construct a protein-protein interaction network. Five genes, UBC, ESR1, EGFR, CUL3, and KRAS, were selected as putative CCR-related factors. Their functions suggested that the combined effects of cellular dyshomeostasis and receptors mediating Aβ toxicity from impaired ubiquitination proteasome system are involved in CCR.Conclusion: This study identified five genes as putative factors and revealed the significance of cellular dyshomeostasis in the CCR of AD. [ABSTRACT FROM AUTHOR]

A modification of the PageRank method based on link ranking is proposed to evaluate the research results of subjects of the scientific space, taking into account selfcitation. The method of reducing the influence of self-citation on the final evaluation of the results of research activity of subjects of the scientific space is described. The evaluation of the results of research is calculated using the modified PR-q method, taking into account self-citation as a solution of a system of linear algebraic equations, matrix of which consists of coefficients determined by the number of citations of publications of one scientist in the publications of another scientist. The described method can be used for the task of evaluating the activity of the components of the scientific space: scientists, higher education institutions and their structural units. For the task of evaluating the research activity of subjects of the scientific space, a method based on link ranking (PageRank method for web pages) and taking into account the selfcitation of scientists is proposed. The latter allows for an adequate assessment, taking into account the abuses associated with the authors’ excessive self-citation. The essence of the constructed method lies in the construction of a system of linear algebraic equations, whose coefficients of the matrix reflect the citations of some scientists by others in the citation network of scientific publications. The value of the coefficients of the matrix of such a system of linear algebraic equations is subject to certain restrictions, which allow to reduce the influence of the factor of excessive self-citation of the author on his overall assessment of research activity. The described method can be used to calculate the complex evaluation of the components of the scientific space: the scientist, the institution of higher education and its separate structural units. Evaluating research results provides an opportunity to verify the relevance of the research process to the goals identified at the planning stage and, if necessary, to adjust the progress of those studies. Also, the calculation of research evaluations of the components (objects and entities) of the scientific space is a powerful tool for managing research projects.

Essential proteins play significant roles in cell survive. In current years, some Protein-Protein Interaction (PPI) data have been discovered in saccharomyces cerevisiae. Due to the high costs of biological experiments, a growing number of computational models are adopted to predict essential proteins. However, the identification accuracy of these computational models still has broad space for improvement. In this paper, a novel prediction model called NPRI is proposed to infer potential essential proteins based on the PageRank algorithm. In NPRI, a new heterogeneous Protein-Domain network will be constructed by integrating three kinds of networks such as the weighted PPI network, the Domain-Domain network and the initial Protein-Domain network first. Here, these three kinds of networks are established in accordance with gene expression data, original PPI network and known Protein-Domain network respectively. Next, based on the newly constructed heterogeneous Protein-Domain network, we will extract functional features and topological characteristics for each protein to further construct a novel distribution rate network. And then, an improved iteration method based on the PageRank algorithm will be implemented on the novel distribution rate network to infer essential proteins. Finally, in order to evaluate the performance of NPRI, we will compare NPRI with other state-of-the-art prediction models, and simulation results show that NPRI can achieve reliable identification accuracies of 90%, 84.5% and 79% in top 100, 200 and 300 predicted candidate essential proteins separately, which outperform these competitive models remarkably, and means that NPRI is a promising framework for identifying essential proteins as well.

Identification of essential proteins is important for understanding cell survival and development, because even if only one of these proteins is missing, organisms cannot survive or develop. Since traditional methods for identifying essential proteins based on biological experiments are costly and inefficient, more and more computational models are proposed for predicting essential proteins in recent years. In this paper, a novel computational model called BSPM is proposed, in which, an original PPI network will be built based on known protein-protein associations first, and then topology information of the original PPI network will be adopted to measure the similarities between proteins based on the SimRank algorithm. Thereafter, a weighted PPI network can be obtained based on the similarities between proteins and the original PPI network. Finally, based on the weighted PPI network, the PageRank algorithm will be used to infer potential essential proteins. Moreover, in order to evaluate the performance of BSPM, we have compared the performance of BSPM with 14 classical prediction models in the field based on two different databases, and experimental results show that BSPM can achieve prediction accuracies of 92%, 81% and 76% out of the top 100, 200 and 300 candidate proteins separately, which not only are significantly better than those 14 competitive classical prediction models, but also means that BSPM can be used as an effective model for identifying essential proteins in the future.

The study of disease-pathway association in human diseases is a perennial focus of the biomedical field. The association of diseases and pathways can help in the discovery of the mechanisms or relationships of human diseases. The accuracy of disease identification has been less than satisfactory despite decades of research in this area. Therefore, this study proposes a computational model for the prediction of disease-pathway associations. The proposed computational model is based on Random Walk with Restart on heterogeneous network (RWRH) and PageRank. The RWRH disease-pathway association model is a novel computational model that can predict potential disease-pathway associations. Furthermore, the model can help pathologists understand the correlations among disease-pathway associations, treatments, and reactions. We performed a pathway-based study to expand disease variation relationships and to find new molecular correlations between genetic mutations. We constructed a biological network on the basis of shared gene interactions of disease-pathways and attempted to investigate the pathogenesis of a disease by analyzing the constructed network. The network construction was based on two parts. First, the similarity between pathway-pathway networks was calculated. Second, a disease-disease (DD) similarity network was constructed, and the correlation between disease and disease similarity was calculated. We also investigated the pathway seed node and disease seed node with high PageRank. Moreover, we focused on mining the complexity of disease-pathway associations. We used the bipartite network of disease-pathway associations to combine the obtained biological information, which was based on the pair similarity of sequence expression weights. These weights, which were obtained by using the multilayer resource-allocation algorithm, were used to calculate the prediction scores of each disease-pathway pair. Here, through leave-one-out cross-validation, we examined a $210\times1855$ matrix, with the 210 rows representing diseases and 1855 columns indicating pathways. The disease-pathway adjacency matrix contained 13,838 known disease-pathway associations. The best predictive results achieved an area-under-the-curve value of 0.8218 and a two-class precision-recall curve. These results indicate that our method has higher scientific performance than previously proposed methods. We predicted pathogen, DD, and disease-pathway relationships by comparing them with known associations and through publication search. We then proposed the possible reasons for our predictions.

The PageRank (PR) algorithm is the base of Google search engine. In this paper, we study the PageRank sequence for undirected graphs of order six by PR vector. Then, we provide an ordering for graphs by variance of PR vector which it’s variation is proportional with variance of degree sequence. Finally, we introduce a relation between domination number and PR-variance of graphs.