Weighted PageRank Algorithm Research Articles

An Automated Analysis Framework for Epidemiological Survey on COVID-19.

For a long time, the prevention and control of COVID-19 has received significant attention. A crucial aspect of controlling the disease's spread is the epidemiological survey of patients and the subsequent analysis of epidemiological survey reports (case reports). However, current mainstream analysis approaches are all made manually. This manual method is time-consuming and manpower-intensive. This paper designs an automated visual epidemiological survey analysis (AVESA) framework for the epidemiological survey on COVID-19. AVESA designs a deep neural network for information extraction from case reports and automatically constructs an epidemiological knowledge graph based on predefined pattern. Moreover, a multi-dimensional knowledge reasoning model is developed for conducting knowledge reasoning in the complete COVID-19 epidemiological knowledge graph. In the entity extraction sub-task and multi-task extraction sub-task, AVESA achieved F1 scores of 85.12% and 92.29% respectively on the constructed dataset, significantly outperforming the standalone information extraction models. In full-graph computing, all three experiments align closely with manual analysis standards. In the risk analysis experiment, the weighted PageRank algorithm showed an average improvement of 11.21% in Top_Recall_n% over the standard PageRank algorithm. In the community detection experiment, the weighted Louvain algorithm showed a mere 4.34% community difference rate compared to manual analysis.

A comprehensive resilience evaluation method for power distribution systems based on weighted Page-Rank algorithm

Resilience evaluation plays a critical role in the decarbonized development of modern power distribution systems since it objectively assesses how the distribution system performs in the face of stochastic renewable generation, increasing load demand, and promising to cut carbon emissions. This study proposed a comprehensive resilience evaluation method for regional distribution systems. The proposed resilience evaluation system comprises three sub-groups: performance, economy, and connections. To improve the objectivity of the proposed evaluation method, the weighting factors are calculated based on the Analytic Hierarchy Process (AHP) and weighted Page-ranking algorithm, respectively. Specifically, AHP is adopted to calculate the weighting factors for different metrics. At the same time, the original Page-ranking algorithm is improved according to the importance of the buses and then used to calculate the weighting factors for different buses within the same metric. The proposed method is applied to a real-world distribution network in China. Numerical results show that the proposed method can objectively evaluate the overall resilience level of the distribution network, including the source side, the network, and the load side. The evaluation results can help the decision-makers to make resilience enhancement in an economical and targeted way.

Performance Evaluation of Reinforcement Learning-Based Adaptive Signal Control Deployment in Urban Network

The adaptive traffic control system (ATCS) has shown great potential to address traffic congestion, fuel consumption, and air pollution issues because of its strong adaption to traffic fluctuation. With the popularity of artificial intelligence, Reinforcement Learning (RL) has been applied to ATCS and achieved excellent performance. In RL-based ATCS, the controller collects traffic state information and learns a better signal control policy by minimizing travel delay. For ATCS of an area-wide network, most existing studies assumed that all intersections are installed with ATCS. However, fully ATCS networks are difficult to achieve in the real world due to the high upgrade cost. Therefore, a practical ATCS deployment strategy in a network is needed for agencies with limited budgets. This study proposes a concept of penetration rate, or the percentage of intersections with ATCS in an area, for intelligent intersections. The weighted PageRank algorithm determines a reasonable deployment plan based on road connectivity and link volume. The relationship between penetration rate and traffic performance is investigated in the SUMO simulation. It reveals that the average stopped and control delays gradually decreased with the penetration rate increase. When the penetration rate reaches 75%, the ATSC performance is about 90% of the optimum. However, inequity and inefficiency exist in non-intelligent intersections regarding average queueing time and length at the penetration rate below 40%. Consequently, the suitable range of penetration rate should be between 40% and 75% to balance the costs and benefits of ATCS deployment with budget constraints.

Ranking locations in a city via the collective home-work relations in human mobility data

Human mobility has long been a hot topic of research in complex systems. An intra-urban network treats a specific set of locations within a city as nodes and the various connections between locations at different spatial scales as edges. Human mobility is one of the direct responses to the connection of locations. Thus, human mobility data can be used to study the importance of locations in intra-urban networks, which has practical applications in urban layout. However, the datasets used in existing studies have shortcomings such as low temporal resolution and narrow range of covered users. Therefore, these kind of datasets only capture partial human mobility behavior. In this paper, a 4G communication data of 1.7million users in Shijiazhuang, a city in northern China, is used to investigate the mobility patterns of users. Our dataset covers a large proportion of the population in the city. We can also identify the location of a user to a high frequency of every second. Then two kinds of directed weighted networks, which consist of links separately from home to workplace and from home to weekend-stay, are respectively constructed based on the users’ mobility record. By analyzing the statistical properties of these networks and their skeleton networks, we find that although most users leave home to work and spend weekends, they prefer to live and work within the same district. To identify important locations in the city, we use the weighted PageRank algorithm to rank nodes in the intra-urban network. We find that the locations in the central area ranked highest, but the ranking is still different from the simple ranking based on population. Finally, using housing prices in the entire urban area of the city, and locations on Zhongshan road, a traditional commercial road in the city, as well as locations on the first ring area, which is the most densely populated area in the city, we verify that in identifying essential locations, PageRank is more effective than only using in-strength, which represents the frequency of a location is visited.

Multi-Ship Encounter Situation Identification and Analysis Based on AIS Data and Graph Complex Network Theory

In order to detect multi-ship encounter situations and improve the safety of navigation, this paper proposed a model which was able to mine multi-ship encounter situations from Automatic identification system (AIS) data and analyze the encounter spatial-temporal process and make collision avoidance decisions. Pairwise encounters identification results and ship motion index were combined into a ship encounter graph network which can use the complex network theory to describe the encounter spatial-temporal process. Network average degree, network average distance and network average clustering coefficient were selected. Based on the recognition results of pairwise encounter identification results, a discrete multi-ship encounter network is constructed. The process of multi-ship encounters from simple to complex to simple is mined based on the process of average network degree from 0 to 0 to obtain a continuous spatial-temporal process. The results can be used for multi-ship encounter situation awareness, multi-ship collision avoidance decision-making and channel navigation evaluation, and also provide data for machine learning. Quaternary dynamic ship domain, fuzzy logic and the weighted PageRank algorithm were used to rank the whole network risk, which is critical to “key ship collision avoidance.” This method overcame the problem that the traditional collision risk evaluation method is only applicable to the difference between two ships and ship perception. The risk rank combined with the artificial potential field method was used. Compared with the traditional artificial potential field method, this method has fewer turns and a smoother trajectory.

ElementRank: Ranking Java Software Classes and Packages using a Multilayer Complex Network-Based Approach

Software comprehension is an important part of software maintenance. To understand a piece of large and complex software, the first problem to be solved is where to start the understanding process. Choosing to start the comprehension process from the important software elements has proven to be a practical way. Research on complex networks opens new opportunities for identifying important elements, and many approaches have been proposed. However, the software networks that existing approaches use neglect the multilayer nature of software systems. That is, nodes in the network can have different types of relationships at the same time, and each type of relationship forms a specific layer. Worse still, they mainly focus on identifying important classes, and little work has been done on quantifying package importance. In this paper, we propose an ElementRank approach to provide a ranked list of classes (or packages) for maintainers to start the comprehension process. The top-ranked classes (or packages) can be seen as the starting points for the software comprehension process at the class (or package) level. First, we introduce two kinds of multilayer software networks to describe the topological structure of software at the class level and package level, respectively. Second, we propose a weighted PageRank algorithm to calculate the weighted PageRank value of classes (or packages) in each layer of the corresponding multilayer software network. Then, we use AHP (Analytic Hierarchy Process) to weigh each layer in the corresponding multilayer software network, and further aggregate the weighted PageRank value to obtain the global weighted PageRank value for each class (or package). Finally, all the classes (or packages) are ranked according to their global weighted PageRank values in a descending order, and the top-ranked classes (or packages) can serve as the starting points for the software comprehension process at the class (or package) level. ElementRank is validated theoretically using the widely accepted Weyuker’s criteria. Theoretical results show that the global weighted PageRank value for classes (or packages) satisfies most of Weyuker’s properties. Furthermore, ElementRank is evaluated empirically using a set of twelve open source software systems. Through a set of experiments, we show the rank correlation between the results of ElementRank and that of the approaches in the related work, and the benefits of ElementRank are also illustrated in comparison with other approaches in the related work. Empirical results also show that ElementRank can be applied to large software systems.

Exploring the effect of city-level collaboration and knowledge networks on innovation: Evidence from energy conservation field

• The study investigates the impact of city-level collaboration and knowledge networks on innovation. • We developed some new indicators of collaboration and knowledge networks that influence innovation. • We calculated cities’ influence force in inter-city collaboration networks by the weighted PageRank algorithm. • Knowledge network embeddedness of a city is measured by embedding depth and embedding breadth. • City-level collaboration and knowledge networks hold distinct structural characteristics and affect innovation in different ways and levels. Collaboration and knowledge networks have been proved to play a crucial role in innovation. From a multilevel network perspective, this study integrates research on the two types of networks and investigates how city-level collaboration and knowledge networks influence innovation in the energy conservation field. To this end, we calculate a city's influence force in its collaboration network based on the weighted PageRank algorithm and propose a novel measurement method of network embedding to gauge the embedding depth and embedding breadth of a city's local knowledge network in the whole knowledge network. Empirical results suggest that a city's aggregation index and influential force in the collaboration network are positively related to its innovation, while geographical distance shows an inverted U-shaped effect. The embedding depth and embedding breadth of a city's local knowledge network have a positive effect, and the structural entropy of its knowledge network generates an inverted U-shaped effect on innovation. Our research contributes to a better understanding of the impact of city-level collaboration and knowledge networks on innovation and points to several general implications for innovation practice and complex network research.

A normalized score-based weighted PageRank algorithm on ranking prediction of basketball games

Ranking of sports teams has always been significant to sponsors, coaches, as well as audiences. Prevailing prediction methods investigate probabilities by taking into account of different kinds of attributes (e.g. field goals, fields goal attempts) in order to establish a detail-based mechanism for analyzing the capability among competing teams. The different types of activation and inhibition actions between athletes provide a considerable challenge in the framework of network analysis. Moreover, these attributes interactions might add up the substantial redundancy to network frame as well. This paper proposes a weighted PageRank algorithm based on the normalized basketball match scores from a macroscopic point of view. Taking Chinese Basketball Association and Chinese University Basketball Association as examples, the developed approach takes into account the win/lose nature of interactions between each pair of competing teams in the framework of PageRank network. We also evolve a weighted network model for the network matrix which highlights the capability difference of teams whose PageRank probabilities are most sensitive with respect to the scores of the two competing teams. The chance of championship of teams can be better demonstrated by the PageRank probabilities. The results show that our method achieves more precise predicting result than that of original PageRank algorithm and Hypertext-Induced Topic Search algorithm.

Study and analysis of category based PageRank method

Internet connectivity is now considered to be a basic human right and in this era of information technology, more efficient ways of browsing the web are being found. The most popular search engine, Google uses its ‘Page Rank Algorithm’ for its searches. In this paper, we aim to find out a more efficient way of searching the web and better organising the search result based on the categories of the pages. The results of the proposed algorithm are compared with the existing Page Rank algorithms, which include the original PageRank Algorithm and a Weighted PageRank Algorithm. The results obtained in this paper have been experimentally compared with the data of the above-mentioned algorithms using comparison graphs.

Unsupervised Keyword Extraction Methods Based on a Word Graph Network

Supervised keyword extraction methods usually require a large human-annotated corpus to train the model. Expensive manual labeling has made unsupervised technology using word graph networks attractive. Traditional word graph networks simply consider the co-occurrence relationship of words or the topological structure of the network, ignoring the influence of semantic relations between words on keyword extraction. To solve these problems, an unsupervised keyword extraction method based on word graph networks for both Chinese and English is proposed. This method uses word embedding to applying a “word attraction score” to semantic relevance between words in a document. Combination of the bias weight of the node and a weighted PageRank algorithm is used to compute the final scores of words. The experimental results demonstrate that the method is more effective than the traditional methods.

Comparative Study of Page Rank and Weighted Page Rank Algorithm

Comparative Study of Page Rank and Weighted Page Rank Algorithm

A novel collaborative requirement prioritization approach to handle priority vagueness and inter-relationships

One of the challenging tasks of requirement engineering is to choose which among the set of requirements are more advantageous than others and ought to be viewed as first for execution. Years of research has established it is vital to capture, analyze and prioritize requirements, which to a great extent relies on acknowledgment acceptance of stakeholder’s requirements, concerns and criteria’s. Proposed method provides interactive support for eliciting stakeholders and developer’s initial ranking decision in collaborative manner. Intuitionistic Fuzzy Approach (IFS) is used to support stakeholders view; whereas analysis of inter relationships, requirement slicing and backtracking are used to support developers view using weighted page rank algorithm. Finally a ranking is generated in a collaborative manner to support requirement prioritization. The feasibility of the approach is illustrated through an experimental proof of concept by comparing results of proposed approach with state of art, Analytic Hierarchy Process (AHP) and Interactive Genetic Algorithm (IGA) prioritization techniques. Results of experimentation conclude that presented approach is capable of producing accurate and comparable results by handling technical constraints of dependency, tradeoffs of collaboration and scope of inclusion of initial multi-criteria preferences expressed as priority values to support reliability and robustness to errors.

A Weighted PageRank-Based Bug Report Summarization Method Using Bug Report Relationships

For software maintenance, bug reports provide useful information to developers because they can be used for various tasks such as debugging and understanding previous changes. However, as they are typically written in the form of conversations among developers, bug reports tend to be unnecessarily long and verbose, with the consequence that developers often have difficulties reading or understanding bug reports. To mitigate this problem, methods that automatically generate a summary of bug reports have been proposed, and various related studies have been conducted. However, existing bug report summarization methods have not fully exploited the inherent characteristics of bug reports. In this paper, we propose a bug report summarization method that uses the weighted-PageRank algorithm and exploits the 'duplicates’, ‘blocks’, and ‘depends-on’ relationships between bug reports. The experimental results show that our method outperforms the state-of-the-art method in terms of both the quality of the summary and the number of applicable bug reports.

A Weighted PageRank-Based Algorithm for Virtual Machine Placement in Cloud Computing

Virtualization makes virtual machine placement (VMP) one of the most important technology in cloud computing. An effective VMP algorithm can significantly improve resource utilization of physical machines (PMs) in cloud datacenters. In this paper, we propose a VMP algorithm based on weighted PageRank (WPRVMP), which pursued to minimize the number of active PMs and improve the total resource utilization of all PMs in cloud datacenters. The impact of unplaced virtual machines (VMs) in the VMP process on the placement results, and the impact of VMP on the total resource utilization of all PMs are considered in this algorithm, to avoid the algorithm to fall into local optimal solution. In the process of selecting PM for each VM, the number of unplaced VMs of different types in the VMP process is considered in WPRVMP by weighted. The weighted PageRank algorithm is used to measure the possibility of a PM making full use of resources under different VMP conditions. The algorithm then ranks PMs according to the impact of the given VM is placed on each PM on the maximum resource utilization of all PMs, and places the VM based on the ranking results. Experimental results show that our algorithm is superior to other algorithms in reducing the number of active PMs and improving the resource utilization of PMs.

Vulnerability analysis of power grid with the network science approach based on actual grid characteristics: A case study in Iran

The vulnerability analysis of power grids has long been of paramount interest to researchers and authorities. Network vulnerability analysis refers to beforehand evaluation of the impact of local failures on the network as a whole so that proper measures could be adopted before occurrence of any major crisis. Previous studies on power grid vulnerabilities that are based on network science concept have been mostly concentrated on topological measures and unweighted networks. In this paper, grid vulnerability identification is carried out by combined use of grid network topology and centrality measures along with real and physical characteristics of power grid. Namely line load and failure rate, with the help of Weighted PageRank algorithm. The main advantage of this approach is that it eliminates complex and time-consuming differential calculations, which results in reduced computation time, and allows real-time updating of the result based on changes in the actual grid characteristics. The proposed model was validated by implementation over a section of Iranian 400 kV and 230 kV power grids. The striking accuracy of the achieved result was confirmed by comparison against results that obtained through calculations by Iran’s national dispatching body.

Comparing the Ranking Performance of Page Rank Algorithm and Weighted Page Rank Algorithm

Comparing the Ranking Performance of Page Rank Algorithm and Weighted Page Rank Algorithm

Effectiveness of Ant Colony Optimization for Weighted Page Rank Algorithm in Web Access

In general the web is growing very rapidly and data generation is also vast and high. Search Engines play an eminent role in retrieving data from web. The user searching for a topic in a web and it retrieves more than hundreds of searchresults as websites. Among the all websites it is difficult for the user to access all the web pages to find relevant information. Weighted Page rank algorithms play a dominant role to make navigation easier to the user. The popularity of a web page depends on the number of its in links and out links and each webpage gets a proportional page rank value. This algorithm considers only link structure not thecontent of the page, so it returns lesssignificant pages to the user query. To overcome the above issues the study focuses on Ant Colony optimization. This study proposes application of ant colony algorithm for modified weighted page rank algorithm. The ACO concept will discovery of redundant components, use clustering based on the structure similarity or web behavior for user and similar WebPages matching. User and webpage similarity matching using Ant colony Optimization based clustering will leads to better access of the webpage in less time and required webpage.

A Survey on Web Structure Mining

These days, World Wide Web gets to be distinctly gigantic data asset. Research Engine likewise gets to be distinctly famous apparatus that help client find required data rapidly. Due to the enormous number of websites and further more website pages, web crawlers assume an essential part in nowadays. One of the fundamental elements that make the distinction of a web index with other is the ranking mechanism (page rank algorithm). In this paper, we will condense some noticeable page rank algorithm and after that we will exhibit our executions that actualize two page rank algorithm PageRank and Weighted Page Rank Algorithm. Keywords: PageRank, algorithm, Weighted, mining

Landmark selection for spectral clustering based on Weighted PageRank

Spectral clustering methods have various real-world applications, such as face recognition, community detection, protein sequences clustering etc. Although spectral clustering methods can detect arbitrary shaped clusters, resulting thus in high clustering accuracy, the heavy computational cost limits their scalability. In this paper, we propose an accelerated spectral clustering method based on landmark selection. According to the Weighted PageRank algorithm, the most important nodes of the data affinity graph are selected as landmarks. Furthermore, the selected landmarks are provided to a landmark spectral clustering technique to achieve scalable and accurate clustering. In our experiments, by using two benchmark face and shape image data sets, we examine several landmark selection strategies for scalable spectral clustering that either ignore or consider the topological properties of the data in the affinity graph. Also, we show that the proposed method outperforms baseline and accelerated spectral clustering methods, in terms of computational cost and clustering accuracy, respectively. Finally, we provide future directions in spectral clustering.

Weighted Page Rank Algorithm based on In-Out Weight of Webpages

In its classical formulation, the well known page rank algorithm ranks web pages only based on in-links between web pages. We propose a new in-out weight based page rank algorithm. In this paper, we have introduced a new weight matrix based on both the in-links and out-links between web pages to compute the page ranks. We have illustrated the working of our algorithm using a web graph. We notice that the page rank values of the web pages computed using the original page rank algorithm and our proposed algorithm are comparable. Moreover, our algorithm is found to be efficient with respect to the time taken to compute the page rank values.