PageRank Research Articles

Transitions between quasi-stationary states in traffic systems: Cologne orbital motorways as an example

Traffic systems can operate in different modes. In a previous work, we identified these modes as different quasi-stationary states in the correlation structure. Here, we analyze the transitions between such quasi-stationary states, i.e. how the system changes its operational mode. In the longer run this might be helpful to forecast the time evolution of correlation patterns in traffic. Taking Cologne orbital motorways as an example, we construct a state transition network for each quarter of 2015 and find a seasonal dependence for those quasi-stationary states in the traffic system. Using the PageRank algorithm, we identify and explore the dominant states which occur frequently within a moving time window of 60 days in 2015. To the best of our knowledge, this is the first study of this type for traffic systems.

Critical Nodes Identification for Power Grid Based on Electrical Topology and Power Flow Distribution

The continuous development of the power industry has driven the increasing scale and complexity of power grids. There are critical nodes within these grids, the failure of which can easily lead to serious power system accidents. In order to effectively identify the critical nodes in a power grid, a critical nodes identification algorithm considering electrical topology and power flow distribution (ETPD-CNIA) is proposed. First, a node interaction model based on the line outage distribution factors is presented. Then, the model is introduced into the PageRank algorithm to calculate the node importance based on the electrical network topology. Finally, considering the node importance based on both the electrical network topology and the actual power flow distribution by an adaptive coefficient, the critical nodes are identified effectively. Simulations on the IEEE 39-bus system and the IEEE 118-bus system show that, compared with existing algorithms, the proposed ETPD-CNIA is more effective and accurate in identifying critical nodes in the power grid.

Analytical comparison of cross impact steady state, DEMATEL, and page rank for analyzing complex systems

Analytical comparison of cross impact steady state, DEMATEL, and page rank for analyzing complex systems

Detecting susceptible communities and individuals in hospital contact networks: a model based on social network analysis

In the real world, the spread of various epidemics can be modelled using the SEIR disease transmission model. Early detection and prevention of susceptible individuals is an effective method of controlling the spread of infectious viruses. This paper presents a study that detects susceptible community on a hospital contact network, which encompasses patients, nurses, doctors and managers. The goal of our work is to identify susceptible communities with patients and healthcare workers, and analyse the independent contact networks of various roles to determine the high-influence nodes within the hospital contact network. If these high-influence nodes are part of the susceptible community, they should be the focus of observation to prevent the spread of the virus. The proposed model combines social network analysis method and machine learning method with the disease transmission model. This study employs the classic overlapping community detection method CPM for community detection and the PageRank algorithm to rank node influence. The experimental results, obtained from real-world hospital contact networks over a 4-day period, demonstrate the effectiveness of the model.

A parallel PageRank algorithm for undirected graph

As a measure of vertex importance according to the graph structure, PageRank has been widely applied in various fields. While many PageRank algorithms have been proposed in the past decades, few of them take into account whether the graph under investigation is directed or not. Thus, some important properties of undirected graph—symmetry on edges, for example—is ignored. In this paper, we propose a parallel PageRank algorithm specifically designed for undirected graphs that can fully leverage their symmetry. Formally, our algorithm extends the Chebyshev Polynomial approximation from the field of real function to the field of matrix function. Essentially, it reflects the symmetry on edges of undirected graph and the density of diagonalizable matrix. Theoretical analysis indicates that our algorithm has a higher convergence rate and requires less computation than the Power method, with a 50% higher convergence rate when the damping factor c=0.85. Experiments on six datasets illustrate that our algorithm with 38 parallelism can be up to 43 times faster than the single-thread Power method.

Innovative measures of student management by counselors in higher education institutions under a big data environment

Abstract Along with the rapid development of big mobile data and the popularization and deepening of education informatization, big data is an irresistible and inescapable new environment and new opportunity, and the way of student management by counselors in higher education institutions is constantly developing and changing. Based on this, the use of big data to promote the innovation of counselor student management in higher education institutions is an inevitable issue to deepen the comprehensive reform in the field of education and a real issue in front of the majority of educators. In this study, the performance of the K-Means algorithm and PageRank are tested from the perspective of big data to find the most effective ways and methods to improve the counselors’ student management work, and the performance and hit rate of each strategy is tested in the application of K-Means. This means application, the LRFU strategy (step = 0) also achieved relatively good performance, using HiBench to run PageRank algorithm test cases. The results showed that the LRU strategy in the PageRank test case also improved the hit rate by 25% compared with other strategies and the hit rate by 13% compared with the LRFU strategy (step = 0). Student management efficiency. Higher education institutions can reform counselors’ student management methods by establishing a large student database management center, strengthening big data analysis, improving the ability of student data mining, processing, and management work, and striving to maximize the efficiency of student management data and information.

Developing a four-entities reinforced rank model to evaluate the topic influence in academic networks

Several studies have reported on metrics for measuring the influence of scientific topics from different perspectives; however, current ranking methods ignore the reinforcing effect of other academic entities on topic influence. In this paper, we developed an effective topic ranking model, 4EFRRank, by modeling the influence transfer mechanism among all academic entities in a complex academic network using a four-layer network design that incorporates the strengthening effect of multiple entities on topic influence. The PageRank algorithm is utilized to calculate the initial influence of topics, papers, authors, and journals in a homogeneous network, whereas the HITS algorithm is utilized to express the mutual reinforcement between topics, papers, authors, and journals in a heterogeneous network, iteratively calculating the final topic influence value. Based on a specific interdisciplinary domain, social media data, we applied the 4ERRank model to the 19,527 topics included in the criteria. The experimental results demonstrate that the 4ERRank model can successfully synthesize the performance of classic co-word metrics and effectively reflect high citation topics. This study enriches the methodology for assessing topic impact and contributes to the development of future topic-based retrieval and prediction tasks.

Can the PageRank centrality be manipulated to obtain any desired ranking?

The significance of the PageRank algorithm in shaping the modern Internet cannot be overstated, and its complex network theory foundations continue to be a subject of research. In this article, we carry out a systematic study of the structural and parametric controllability of PageRank’s outcomes, translating a spectral graph theory problem into a geometric one, where a natural characterization of its rankings emerges. Furthermore, we show that the change of perspective employed can be applied to the biplex PageRank proposal, performing numerical computations on both real and synthetic network datasets to compare centrality measures used.

PageRank 알고리즘 활용한 태권도 선수들의 랭킹 산정

The purpose of this study is to apply the Olympic Kyorugi Ranking system and PageRank algorithm to the results of four world-class taekwondo competitions held from 2020 to 2023, including G20, G14, G10, and G6, to calculate the rankings. Based on the results, a comparison between the two algorithms is conducted to provide basic information for taekwondo ranking calculation methods. For the purpose of this study, information was collected from the official website of the World Taekwondo Federation. Use MS-Excel, PyCharm, and IBM SPSS (ver. 27) programs to process data. The results of this study are as follows. Firstly, using the PageRank algorithm, the gender and weight rankings of taekwondo athletes can be calculated. Secondly, using the PageRank system for ranking calculation is more suitable for considering the relative performance of players than using the Olympic Kyorugi Ranking system for ranking calculation. Thirdly, the ranking consistency calculated using the Olympic Kyorugi Ranking system and PageRank system is relatively low.

Fault propagation analysis of computer numerically controlled machine tools

Computer numerically controlled machine tools are the key equipment of the intelligent manufacturing industry, unscheduled shutdowns can substantially cause equipment damage and production loss. Fault propagation analysis, as a prominent basis of reducing unscheduled downtime, can locate the harmful parts and reveal the fault propagation path. To improve reliability, this paper proposes a novel fault propagation analysis method that can accurately and timely evaluate fault propagation coefficients of components in interior computer numerically controlled machine tools. To depict the latent cause–effect relationships of components, a quantitative causal diagram is built with the aid of design information and topology. An inverse PageRank algorithm is proposed to assess the fault propagation risks of components. Unlike the traditional PageRank algorithm, the idea of inverse PageRank is that a node is crucial if it directs to other important nodes. Subsequently, the fault propagation coefficients of components in the quantitative causal diagram are derived and the fault propagation path is identified. The proposed method is validated on the computer numerically controlled machine tool, and the results are basically consistent with the typical cascading events and conform to expert judgment.

Graph-Based Root Cause Localization in Microservice Systems with Protection Mechanisms

Service anomalies are difficult to locate accurately due to their propagation through service dependencies in microservice systems. Besides, the protection mechanisms are introduced into the microservice systems to ensure the stable operation of services. However, the existing approaches ignore the impact of protection mechanisms on the root cause localization of abnormal services. Specifically, the circuit breaking and rate limiting mechanisms can refuse service requests and thus change the way of anomaly propagation. Moreover, the different service request frequencies and latency make service dependencies change dynamically, resulting in the different probabilities of anomaly propagation among services. In this paper, we propose a novel framework named MicroGBPM to locate the root cause of abnormal services. We model the anomaly propagation among services as a dynamically constructed service attributed graph with metrics and traces when a failure occurs. To eliminate the impact of the protection mechanisms, we design a two-stage dynamic calibration strategy to adjust the probability of anomaly propagation among services. Then, we propose a random walking approach to calculate the root cause results by using the PageRank algorithm. The experimental results show that MicroGBPM improves the accuracy of root cause localization compared to other approaches in the microservice systems with protection mechanisms.

Supply chain resilience in developing countries: a bibliometric analysis and future research directions

PurposeUsing bibliometric, this work aims to evaluate the current status of the body of research on the subject of supply chain resilience (SCR) in developing nations and to provide recommendations for potential lines of inquiry for additional research.Design/methodology/approachThree hundred and thirty-six articles published between 2011 and 2021 were retrieved in Scopus for this bibliometric analysis. This analysis focuses on SCR research conducted in developing countries, highlighting its key authors, countries, institutions, journals, articles and themes.FindingsThis bibliometric review seeks to enrich the discourse on SCR in developing nations through a comprehensive and detailed review of 336 articles covering 138 Journals, 73 countries, 877 authors, 743 organizations and 1,145 author keywords. The United Kingdom, India, and the United States provided a substantial share of the publications. Publication-wise, Hong Kong Polytechnic University and The University of Hong Kong play key roles. The author found that supply chain risk management, sustainability, agile management, artificial intelligence and blockchain are trending topics. Additionally, the author identified eight themes by page rank analysis.Practical implicationsThis study's importance lies primarily in its examination of the current information about SCR in developing countries and significant cluster areas (themes). In the paths, it recommends for further study, which academics may take, and industry professionals should apply in their businesses to create a more resilient and sustainable supply chain.Originality/valueBased on published studies, this study gives exploratory data on SCR in developing countries context. This is first of its kind bibliometric study that focuses on developing countries.

The node importance evaluation method based on graph convolution in multilayer heterogeneous networks

ABSTRACT Node importance evaluation is a hot issue in complex network analysis. Existing node importance evaluation methods are mainly oriented to homogeneous networks, which ignore the heterogeneity of node types and edges. We propose an MLN critical node evaluation method based on graph convolution. In this paper, we generate the feature matrix of nodes. Considering the diversity of node types in the network, we design an adapted node sampling method based on the meta path. An MLN node embedding model is constructed based on a graph convolutional network (MGC). Besides, the negative sampling technique is used to complete MGC training. Metrics of critical node evaluation are constructed by combining the node embedding vectors and local structural features to evaluate the node's importance. The experimental results show that the proposed method has better evaluation accuracy than the K-Shell algorithm (K-Shell), K-shell-based gravity model ranking algorithm (KSDG), the Page Rank algorithm in MLN (PR), influence maximization based on network embedding (IMNE) and the node ranking algorithm based on information entropy (ERM).

An effective fault localization approach based on PageRank and mutation analysis

Mutation-based fault localization (MBFL) is a popular method based on mutation testing. MBFL applies a variety of operators to generate mutants and calculates the statement’s suspiciousness by counting the execution results of the test cases on the mutants. However, the tie problem of MBFL creates obstacles to accurate fault localization. The tie problem refers to that many statements have the same suspiciousness. To solve the tie problem, we propose a fault localization approach based on the PageRank algorithm and mutation analysis (PRMA). We first apply the PageRank algorithm to calculate the faultiness scores of the statements. Then, we weight the suspicious value of the statements with faultiness scores to solve the tie problem. Finally, the weighted suspicious values are sorted in descending order to generate a list, which is provided to developers for fault localization. To evaluate our approach, we conduct experiments on the real fault benchmark Defects4J and the artificial fault dataset Siemens. We compare PRMA with the traditional MBFL techniques (Metallaxis and MUSE) and recently proposed MBFL methods (MCBFL-hybrid-avg, SMFL and SMBFL). The experimental results show that our approach outperforms above comparison methods and improves the effect of fault localization in both quantity and accuracy.

Penerapan Search Engine Optimization (SEO) Pada Strategi Digital Marketing UMKM

In the current digital era, MSMEs business competition is getting tougher in getting customers through digital marketing platforms. The best strategies and solutions are needed by MSMEs to gain market share by optimizing several search engine techniques. Digital marketing in the form of websites that rank well on Google search pages will get more potential visitors. The website must be capable of being featured on Google pages. One way to optimize a website is by using search engine optimization (SEO) techniques to get a good ranking on search engine pages. SEO techniques are needed to increase brand awareness and generate potential traffic for a company's services or products. In this study, the authors apply SEO to MSMEs engaged in florist business. This research is an implementative research with SEO Web Design Methodology method, which emphasizes the delivery of information precisely and quickly. The result of implementing the system development method carried out on the digital marketing platform software from Funcraft Florist is that the Funcraft.epizy.com website can appear on the initial ranking of Google pages after applying SEO techniques according to keywords.

Meeting Summarizer Using Natural Language Processing

Abstract: Meeting transcripts produced by tools like Microsoft Teams and Google Meet, are useful for recording discussions and decisions made during meetings. However, reading through long transcripts can be time-consuming and may not always be the most efficient way to understand the key points and conclusions of a meeting. Meeting summarization is a subfield of natural language processing that can extract important information from meeting transcripts and generate a concise summary. This summary can be used to quickly understand the key points and conclusions of the meeting, and can be especially useful for stakeholders who were not able to attend the meeting in person. Several natural language processing techniques can be used to create summaries of meeting transcripts, such as the term frequency-inverse document frequency (TF-IDF) method, PageRank algorithm, Named Entity Recognition, Topic Modeling and specific summarization algorithms. Each technique has its own advantages and limitations, and the appropriate technique can be chosen based on the specific needs and requirements of the organization, such as accuracy, efficiency, and customization.

An edge re-ordering based acceleration architecture for improving data locality in graph analytics applications

Data structure is the key in Edge Computing where various types of data are continuously generated by ubiquitous devices. Within all common data structures, graphs are used to express relationships and dependencies among human identities, objects, and locations. They are also expected to become one of the most important data infrastructures in the near future. Furthermore, as graph processing often requires random accesses to vast memory spaces, conventional memory hierarchies with caches cannot work efficiently. To alleviate such memory access bottlenecks in graph processing, we present a solution through vertex accesses scheduling and edge array re-ordering, in parallel with the execution of graph processing applications to improve both temporal and spatial locality of memory accesses, especially for edge-centric graph analytics which are popular means in handling dynamic graphs. Our proposed architecture is evaluated and tested through both trace-based cache simulations and cycle-accurate FPGA-based prototyping. Evaluation results show that our proposal has a potential of significantly reducing the Last Level Cache (LLC) misses by 62.60% in general among PageRank and BFS algorithms. Meanwhile, evaluations with the FPGA prototype successfully reduce the quantity of Miss-Per-Kilo-Instructions (MPKI) for LLC by 56.27% on average.

Identifying Important Nodes in Trip Networks and Investigating Their Determinants.

Describing travel patterns and identifying significant locations is a crucial area of research in transportation geography and social dynamics. Our study aims to contribute to this field by analyzing taxi trip data from Chengdu and New York City. Specifically, we investigate the probability density distribution of trip distance in each city, which enables us to construct long- and short-distance trip networks. To identify critical nodes within these networks, we employ the PageRank algorithm and categorize them using centrality and participation indices. Furthermore, we explore the factors that contribute to their influence and observe a clear hierarchical multi-centre structure in Chengdu's trip networks, while no such phenomenon is evident in New York City's. Our study provides insight into the impact of trip distance on important nodes within trip networks in both cities and serves as a reference for distinguishing between long and short taxi trips. Our findings also reveal substantial differences in network structures between the two cities, highlighting the nuanced relationship between network structure and socio-economic factors. Ultimately, our research sheds light on the underlying mechanisms shaping transportation networks in urban areas and offers valuable insights into urban planning and policy making.

Modeling the systemic trade risk of crude oil from a network perspective, combined with a sentiment proxy

Introduction: Trade networks of crude oil are susceptible to cascade of initial shocks that increase systemic trade risks and threaten energy security. This study introduces a novel method of modelling systemic trade risk of crude oil which is combined with a sentiment proxy from a network perspective.Method: We construct sentiment instability to evaluate the uncertainty of crude oil trade in a country and use the PageRank algorithm to measure supplier diversity from a network perspective instead of direct trade partners.Result: At the global level, we show that the distribution of systemic oil trade risk has an obvious heterogeneity and a significant negative correlation between systemic trade risk of crude oil and trade volume volatility. At the regional level, we compare the systemic trade risk of crude oil between China and America and systemic trade risks show strongly significant correlations with the price volatility in both China and America. Furthermore, the structure of trade network can effectively reduce the systemic risk in America while it increases the systemic trade risk in China.Discussion: Our results can give a reason for an irrational practice of Chinese crude oil imports which are “buying when the price is rising and not buying when the price is declining”.

Distributed large-scale graph processing on FPGAs

Processing large-scale graphs is challenging due to the nature of the computation that causes irregular memory access patterns. Managing such irregular accesses may cause significant performance degradation on both CPUs and GPUs. Thus, recent research trends propose graph processing acceleration with Field-Programmable Gate Arrays (FPGA). FPGAs are programmable hardware devices that can be fully customised to perform specific tasks in a highly parallel and efficient manner. However, FPGAs have a limited amount of on-chip memory that cannot fit the entire graph. Due to the limited device memory size, data needs to be repeatedly transferred to and from the FPGA on-chip memory, which makes data transfer time dominate over the computation time. A possible way to overcome the FPGA accelerators’ resource limitation is to engage a multi-FPGA distributed architecture and use an efficient partitioning scheme. Such a scheme aims to increase data locality and minimise communication between different partitions. This work proposes an FPGA processing engine that overlaps, hides and customises all data transfers so that the FPGA accelerator is fully utilised. This engine is integrated into a framework for using FPGA clusters and is able to use an offline partitioning method to facilitate the distribution of large-scale graphs. The proposed framework uses Hadoop at a higher level to map a graph to the underlying hardware platform. The higher layer of computation is responsible for gathering the blocks of data that have been pre-processed and stored on the host’s file system and distribute to a lower layer of computation made of FPGAs. We show how graph partitioning combined with an FPGA architecture will lead to high performance, even when the graph has Millions of vertices and Billions of edges. In the case of the PageRank algorithm, widely used for ranking the importance of nodes in a graph, compared to state-of-the-art CPU and GPU solutions, our implementation is the fastest, achieving a speedup of 13 compared to 8 and 3 respectively. Moreover, in the case of the large-scale graphs, the GPU solution fails due to memory limitations while the CPU solution achieves a speedup of 12 compared to the 26x achieved by our FPGA solution. Other state-of-the-art FPGA solutions are 28 times slower than our proposed solution. When the size of a graph limits the performance of a single FPGA device, our performance model shows that using multi-FPGAs in a distributed system can further improve the performance by about 12x. This highlights our implementation efficiency for large datasets not fitting in the on-chip memory of a hardware device.