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There have been very few studies done on measuring the influence of all prefecture‐level cities on a national scale due to the limited availability of public data, challenges in data collection, and insufficient data comprehensiveness. In this paper, we aim to fill this gap by investigating this problem in China. We first collected 692,859 news articles spanning one full year from the WeChat Official Accounts of 339 cities and Taiwan Province, which served as our study area and dataset. Then, we developed a city extractor module to reduce the ambiguity of place names and constructed a city interaction network. Then, we modeled the City Influence Index (CII) and the intensity of its influence. Finally, we proposed an analytical framework that examines the relationship between CII and Gross Domestic Product (GDP), compares it with the Global Cities Index, conducts influence analysis of cities at different levels, and more. The experimental results demonstrate that our analytical framework can effectively measure the influence of cities on a national scale and uncover the implicit relationships between cities. In doing so, our study offers a new perspective for measuring city influence. Code is available at: https://github.com/vczero/CII. [ABSTRACT FROM AUTHOR]

Over the past few decades, the study of leadership theory has expanded across various disciplines, delving into the intricacies of human behavior and defining the roles of individuals within organizations. Its primary objective is to identify leaders who play significant roles in the communication flow. In addition, behavioral theory posits that leaders can be distinguished based on their daily conduct, while social network analysis provides valuable insights into behavioral patterns. Our study investigates five and six types of social networks frequently observed in different organizations. This study is conducted using datasets we collected from an IT company and public datasets collected from a manufacturing company for the thorough evaluation of prediction performance. We leverage PageRank and effective word embedding techniques to obtain novel features. State-of-the-art performance is obtained using various statistical machine learning methods, graph convolutional networks (GCN), automated machine learning (AutoML), and explainable artificial intelligence (XAI). More specifically, our approach can achieve state-of-the-art performance with an accuracy close to 90 % for leaders identification with data from projects of different types. This investigation contributes to the establishment of sustainable leadership practices by aiding organizations in retaining their leadership talent. [ABSTRACT FROM AUTHOR]

PageRank can be viewed as a hyperlink-based method for estimating the importance of nodes in a network, and has attracted a lot of attention from researchers. In this paper, we first propose an extrapolation procedure for PageRank vector estimation based on the linear combination of the Ritz values computed from the Hessenberg process and eigenvectors from the classical power method. In order to improve the convergence rate of the PageRank computations, the extrapolation procedure is introduced into the Hessenberg-type algorithm by Gu et al. (Numer. Algorithms, 89(4): 1845–1863, 2022), then a new algorithm is derived and named Hessenberg-extrapolation algorithm whose convergence and construction are investigated in detail. In addition, we further analyze the convergence of the Hessenberg-type algorithm and discuss the relationship between the approximate vectors of the Hessenberg-type algorithm and the Arnoldi-type algorithm. Numerical experiments on several examples demonstrate that our presented algorithm has great potential in PageRank approximation. [ABSTRACT FROM AUTHOR]

Precisely recommending relevant items to users is a challenging task because the user’s rating can be influenced by various features. Therefore, social recommender systems have recently been introduced to leverage both the user-item interaction graph and the user-user social relation graph for more accurate rating predictions. Moreover, as graph neural networks (GNN) have demonstrated superior performance in graph representation learning, several algorithms have been developed to incorporate GNN into social recommender systems. However, when the sizes of the social graph and user-item graph are very large, the computational demands of existing GNN-based social recommender systems for aggregating user and item nodes becomes the primary bottleneck. In this paper, we develop a novel lightweight GNN-based social recommender system (called LiteGSR) that effectively reduces the computational overhead associated with aggregation operations while maintaining accuracy. To achieve this, we propose a new approach for refining the social graph by utilizing PageRank-based centrality scores of users and adapting representative virtual users in the user-item graph. Experimental results demonstrate that our new social recommender systems outperform existing state-of-the-art recommender systems in both accuracy and training time. [ABSTRACT FROM AUTHOR]

In recent years, the PageRank algorithm has garnered significant attention due to its crucial role in search engine technologies and its applications across various scientific fields. It is well-known that the power method is a classical method for computing PageRank. However, there is a pressing demand for alternative approaches that can address its limitations and enhance its efficiency. Specifically, the power method converges very slowly when the damping factor is close to 1. To address this challenge, this paper introduces a modified multi-step splitting iteration approach for accelerating PageRank computations. Furthermore, we present two variants for computing PageRank, which are variants of the modified multi-step splitting iteration approach, specifically utilizing the thick restarted Arnoldi and adaptively accelerated Arnoldi methods. We provide detailed discussions on the construction and theoretical convergence results of these two approaches. Extensive experiments using large test matrices demonstrate the significant performance improvements achieved by our proposed algorithms. [ABSTRACT FROM AUTHOR]

Non-backtracking PageRank is a variation of Google's PageRank, which is based on non-backtracking random walk. However, if the number of dangling nodes of a graph is large, the non-backtracking PageRank algorithm proposed in [F. Arrigo, D. Higham, and V. Noferini, Non-backtracking PageRank, Journal of Scientific Computing, 80: 1419–1437, 2019] may suffer from huge memory requirements and heavy computational costs. Thus, the non-backtracking PageRank algorithm is only applicable to small-scale or medium-sized graphs with few dangling nodes. In this work, we first consider how to compute the non-backtracking PageRank vector efficiently by using the Jacobi iteration, and then propose two strategies to speed up the computation of non-backtracking PageRank, in which we add some edges to a graph in a randomized and a fixed way, respectively. The computational issues are discussed in detail. The advantages of the proposed algorithms are two-fold. First, the sizes of the matrix computation problems are much smaller than that of the original one. Second, there is no kronecker product in the involved non-backtracking edge matrices, and the structures of the non-backtracking PageRank problems are greatly simplified. Comprehensive numerical experiments are performed on some real-world network matrices, which show that the solutions obtained from the two proposed algorithms and that from the original non-backtracking PageRank algorithm are highly correlated, while the two proposed algorithms can be tens or even hundreds times faster than their original counterpart. [ABSTRACT FROM AUTHOR]

Abstract With the ongoing biodiversity crisis, identifying which species are of particular importance to prevent the extinction of other species has become a pressing issue. However, most approaches to detect these important species are made at a local (i.e, community) level, without considering the potential effect of species dispersion in a landscape. As habitat fragmentation has important effects on biodiversity, we need methods to better assess how local and spatial processes interact to determine important species and therefore better inform conservation efforts. We present a modified PageRank algorithm to determine the importance of species in meta‐communities. Species importance is defined as the ability of species in the meta‐community to spread nutrients within two sets of networks: food webs that depict local trophic interactions and landscape networks representing the movement of species across different habitat patches. We show that dispersal and trophic links jointly determine the importance of the different species, both at the local scale (within habitat patches) and at larger scales (landscape). More precisely, we observed that (i) what is considered an important species changes between isolated communities and meta‐communities and (ii) the importance of a species in a meta‐community depends on the position of its habitat patch in the landscape network. The importance of a species is influenced by both intrinsic factors (dispersal capacity, trophic position) and extrinsic factors (position of the patch in the landscape network). Our results stress the need for a larger‐scale consideration of space in the identification of important species.

Social media technology affects the learning paradigm change towards social media-based learning, known as social e-learning. Social e-learning regards a person as a center of learning, dubbed people-centered learning. Here, people are encouraged to interact or communicate with others and produce their learning content. This work attempted to provide a solution model for rural e-learning social learning empowered with intelligent web technology. The proposed social e-learning includes several modules for development, such as personal space, collaboration space, and communication space modules. It also leverages intelligent web technologies currently implemented in today’s social media applications, such as article search, article recommendations, friendship recommendations, and document classification. In the searching module, the PageRank method was used to calculate the relevance score to determine the rating of the documents or articles. The similarity-based element calculation method was utilized to create articles’ suggestions and recommendations. The naïve Bayes algorithm, decision tree, and neural network were compared to find the best solution for article classification in agriculture, fisheries, animal husbandry, and plantations. When comparing these three algorithms, the result showed that the neural network was the most accurate classification, reaching 95.2% accuracy. A clustering algorithm, namely robust clustering using links (ROCK), was utilized for rural friendship recommendation. Thus, these algorithms (the PageRank, the similarity-based element, neural network, and the ROCK) were suitable and recommended for supporting intelligent web paradigms in social e-learning applications.

Google employs PageRank to rank web pages, determining the order in which search results are presented to users based on their queries. PageRank is primarily utilized for directed networks, although there are instances where it is also applied to undirected networks. In this paper, we have applied PageRank to undirected networks, showing that a vertex's PageRank relies on its initial value, often referred to as an intrinsic, non-network contribution. We have analytically proved that when the initial value of vertices is either proportional to their degrees or set to zero, the PageRank values of the vertices become directly proportional to their degrees. Simulated and empirical data are employed to bolster our research findings. Additionally, we have investigated the impact of initial values on PageRank localization. [ABSTRACT FROM AUTHOR]

Many ranking algorithms and metrics have been proposed to identify high-impact papers. Both the direct citation counts and the network-based PageRank-like algorithms are commonly used. Ideally, the more complete the data on the citation network, the more informative the ranking. However, obtaining more data on citation relations is often costly and challenging. In some cases, obtaining the citation counts can be relatively simple. In this paper, we look into using the additional citation counts but without additional citation relations to form more informative metrics for identifying high-impact papers. As an example, we propose enhancing the original PageRank algorithm by combining the local citation network with the additional citation counts from a more complete data source. We apply this enhanced method to American Physical Society (APS) papers to verify its effectiveness. The results indicate that the proposed ranking algorithm is robust against missing data and can improve the identification of high-quality papers. This shows that it is possible to enhance the effectiveness of a network-based metric calculated on a relatively small citation network by including only the additional data of the citation counts, without the additional citation relations. [ABSTRACT FROM AUTHOR]

With the ongoing biodiversity crisis, identifying which species are of particular importance to prevent the extinction of other species has become a pressing issue. However, most approaches to detect these important species are made at a local (i.e, community) level, without considering the potential effect of species dispersion in a landscape. As habitat fragmentation has important effects on biodiversity, we need methods to better assess how local and spatial processes interact to determine important species and therefore better inform conservation efforts.We present a modified PageRank algorithm to determine the importance of species in meta‐communities. Species importance is defined as the ability of species in the meta‐community to spread nutrients within two sets of networks: food webs that depict local trophic interactions and landscape networks representing the movement of species across different habitat patches.We show that dispersal and trophic links jointly determine the importance of the different species, both at the local scale (within habitat patches) and at larger scales (landscape). More precisely, we observed that (i) what is considered an important species changes between isolated communities and meta‐communities and (ii) the importance of a species in a meta‐community depends on the position of its habitat patch in the landscape network.The importance of a species is influenced by both intrinsic factors (dispersal capacity, trophic position) and extrinsic factors (position of the patch in the landscape network). Our results stress the need for a larger‐scale consideration of space in the identification of important species. [ABSTRACT FROM AUTHOR]

A network evolution with predicted tail and extremal indices of PageRank and the Max-Linear Model used as node influence indices in random graphs is considered. The tail index shows a heaviness of the distribution tail. The extremal index is a measure of clustering (or local dependence) of the stochastic process. The cluster implies a set of consecutive exceedances of the process over a sufficiently high threshold. Our recent results concerning sums and maxima of non-stationary random length sequences of regularly varying random variables are extended to random graphs. Starting with a set of connected stationary seed communities as a hot spot and ranking them with regard to their tail indices, the tail and extremal indices of new nodes that are appended to the network may be determined. This procedure allows us to predict a temporal network evolution in terms of tail and extremal indices. The extremal index determines limiting distributions of a maximum of the PageRank and the Max-Linear Model of newly attached nodes. The exposition is provided by algorithms and examples. To validate our theoretical results, our simulation and real data study concerning a linear preferential attachment as a tool for network growth are provided. [ABSTRACT FROM AUTHOR]

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The growth in academic and scientific publications has increased very rapidly. Researchers must choose a representative and significant literature for their research, which has become challenging worldwide. Usually, the paper citation number indicates this paper's potential influence and importance. However, this standard metric of citation numbers is not suitable to assess the popularity and significance of recently published papers. To address this challenge, this study presents an effective prediction method called FutureCite to predict the future citation level of research articles. FutureCite integrates machine learning with text and graph mining techniques, leveraging their abilities in classification, datasets in-depth analysis, and feature extraction. FutureCite aims to predict future citation levels of research articles applying a multilabel classification approach. FutureCite can extract significant semantic features and capture the interconnection relationships found in scientific articles during feature extraction using textual content, citation networks, and metadata as feature resources. This study's objective is to contribute to the advancement of effective approaches impacting the citation counts in scientific publications by enhancing the precision of future citations. We conducted several experiments using a comprehensive publication dataset to evaluate our method and determine the impact of using a variety of machine learning algorithms. FutureCite demonstrated its robustness and efficiency and showed promising results based on different evaluation metrics. Using the FutureCite model has significant implications for improving the researchers' ability to determine targeted literature for their research and better understand the potential impact of research publications. [ABSTRACT FROM AUTHOR]

The object of this study is the processes related to the assessment of the closeness of publication ties among scientists and taking into account their productivity related to scientific activity. This is necessary to increase the efficiency of management of research projects. To this end, the PR, TWPR, TWPR-CI methods for calculating scientific productivity estimates of scientists were described. In particular, the TWPR-CI method gives preference to those scientists whose works were more intensively published and cited during the last period of time, which is important for the formation of the composition of the executors of scientific research projects. The method for calculating the closeness of publication ties among scientists or average asymmetric tie strength was also described. The verification of dependence between the evaluation of the closeness of publication ties among scientists and their scientific productivity was carried out based on the analysis of the citation network of scientific publications and the network of scientific cooperation. The networks are built on the basis of the open access Citation Network Dataset (ver. 14). The dataset contains information on more than 5 million scientific publications and more than 36 million citations to them. The correlation analysis revealed the presence of a weak inverse relationship between these estimates. However, the weakness of the connection allows us to state that for this case there is no established correlation between the assessment of scientific productivity and the assessment of the closeness of publication ties. That is, the hypothesis that the weak connection between scientists makes it possible to increase the productivity and innovativeness of their publications was not confirmed. The results allow for a systematic approach to the process of evaluation and planning of the results of research projects, as well as the formation of the composition of their executors. [ABSTRACT FROM AUTHOR]

In this study, we explore how transformer models, which are known for their attention mechanisms, can improve pathogen prediction in pastured poultry farming. By combining farm management practices with microbiome data, our model outperforms traditional prediction methods in terms of the F1 score—an evaluation metric for model performance—thus fulfilling an essential need in predictive microbiology. Additionally, the emphasis is on making our model's predictions explainable. We introduce a novel approach for identifying feature importance using the model's attention matrix and the PageRank algorithm, offering insights that enhance our comprehension of established techniques such as DeepLIFT. Our results showcase the efficacy of transformer models in pathogen prediction for food safety and mark a noteworthy contribution to the progress of explainable AI within the biomedical sciences. This study sheds light on the impact of effective farm management practices and highlights the importance of technological advancements in ensuring food safety. [ABSTRACT FROM AUTHOR]

This research leverages Memgraph, an open-source graph database, to analyze graph-based network data and apply Graph Neural Networks (GNNs) for a detailed classification of cyberattack tactics categorized by the MITRE ATT&CK framework. As part of graph characterization, the page rank, degree centrality, betweenness centrality, and Katz centrality are presented. Node classification is utilized to categorize network entities based on their role in the traffic. Graph-theoretic features such as in-degree, out-degree, PageRank, and Katz centrality were used in node classification to ensure that the model captures the structure of the graph. The study utilizes the UWF-ZeekDataFall22 dataset, a newly created dataset which consists of labeled network logs from the University of West Florida's Cyber Range. The uniqueness of this study is that it uses the power of combining graph-based characterization or analysis with machine learning to enhance the understanding and visualization of cyber threats, thereby improving the network security measures. [ABSTRACT FROM AUTHOR]

Co-evolving network models, wherein dynamics such as random walks on the network influence the evolution of the network structure, which in turn influences the dynamics, are of interest in a range of domains. While much of the literature in this area is currently supported by numerics, providing evidence for fascinating conjectures and phase transitions, proving rigorous results has been quite challenging. We propose a general class of co-evolving tree network models driven by local exploration, started from a single vertex called the root. New vertices attach to the current network via randomly sampling a vertex and then exploring the graph for a random number of steps in the direction of the root, connecting to the terminal vertex. Specific choices of the exploration step distribution lead to the well-studied affine preferential attachment and uniform attachment models, as well as less well understood dynamic network models with global attachment functionals such as PageRank scores (Chebolu and Melsted, in: SODA, 2008). We obtain local weak limits for such networks and use them to derive asymptotics for the limiting empirical degree and PageRank distribution. We also quantify asymptotics for the degree and PageRank of fixed vertices, including the root, and the height of the network. Two distinct regimes are seen to emerge, based on the expected exploration distance of incoming vertices, which we call the 'fringe' and 'non-fringe' regimes. These regimes are shown to exhibit different qualitative and quantitative properties. In particular, networks in the non-fringe regime undergo 'condensation' where the root degree grows at the same rate as the network size. Networks in the fringe regime do not exhibit condensation. Further, non-trivial phase transition phenomena are shown to arise for: (a) height asymptotics in the non-fringe regime, driven by the subtle competition between the condensation at the root and network growth; (b) PageRank distribution in the fringe regime, connecting to the well known power-law hypothesis. In the process, we develop a general set of techniques involving local limits, infinite-dimensional urn models, related multitype branching processes and corresponding Perron–Frobenius theory, branching random walks, and in particular relating tail exponents of various functionals to the scaling exponents of quasi-stationary distributions of associated random walks. These techniques are expected to shed light on a variety of other co-evolving network models. [ABSTRACT FROM AUTHOR]

Evaluating academic papers and groups is important in scholar evaluation and literature retrieval. However, current evaluation indices, which pay excessive attention to the citation number rather than the citation importance and unidirectionality, are relatively simple. This study proposes new evaluation indices for papers and groups. First, an improved PageRank (PR) algorithm introducing citation importance is proposed to obtain a new citation-based paper index (CPI) via a pre-ranking and fine-tuning strategy. Second, to evaluate the paper's influence inside and outside its research field, the focus citation-based paper index (FCPI) and diversity citation-based paper index (DCPI) are proposed based on topic similarity and diversity, respectively. Third, aside from the statistical indices for academic papers, we propose a foreign academic degree of dependence (FAD) to characterise the dependence between two academic groups. Finally, artificial intelligence (AI) papers from 2005 to 2019 are utilised for a case study. [ABSTRACT FROM AUTHOR]

The PageRank model, which was first proposed by Google for its web search engine application, has since become a popular computational tool in a wide range of scientific fields, including chemistry, bioinformatics, neuroscience, bibliometrics, social networks, and others. PageRank calculations necessitate the use of fast computational techniques with low algorithmic and memory complexity. In recent years, much attention has been paid to Krylov subspace algorithms for solving difficult PageRank linear systems, such as those with large damping parameters close to one. In this article, we examine the full orthogonalization method (FOM). We present a convergence study of the method that extends and clarifies part of the conclusions reached in Zhang et al. (J Comput Appl Math. 2016; 296:397–409.). Furthermore, we demonstrate that FOM is breakdown free when solving singular PageRank linear systems with index one and we investigate the effect of using weighted inner‐products instead of conventional inner‐products in the orthonormalization procedure on FOM convergence. Finally, we develop a shifted polynomial preconditioner that takes advantage of the special structure of the PageRank linear system and has a good ability to cluster most of the eigenvalues, making it a good choice for an iterative method like FOM or GMRES. Numerical experiments are presented to support the theoretical findings and to evaluate the performance of the new weighted preconditioned FOM PageRank solver in comparison to other established solvers for this class of problem, including conventional stationary methods, hybrid combinations of stationary and Krylov subspace methods, and multi‐step splitting strategies. [ABSTRACT FROM AUTHOR]

There is a vast quantity of information contained within the billions of web pages that make up the World Wide Web (WWW). Search engines carry out a variety of activities depending on their own architectures for retrieving the necessary information from the WWW. The search engine typically returns a huge number of pages in response to a user's query when the user submits one. Numerous ranking techniques have been utilized on search results to aid consumers in navigating the result list. The majority of ranking algorithms described in literature are either content-based or link-based, and they do not take user usage patterns into account. The presented study discusses web mining ranking algorithms depending on structures, contents, and usages and suggests a new ranking method to assess the significance of links with the use of semantic metadata analysis, which considers the number of links visited across nearly all regions, time periods, and related topics and queries. Furthermore, the suggested system uses the user's query to find more relevant information. The most valuable pages are thus displayed at the top of the result list based on user browsing behavior, which significantly decreases the search space. Results showed better ranking output based on different criteria, such as the number of links visited yearly, the number of links visited hourly, the number of links visited by region, the number of links visited by related topics, and the number of links visited by related queries. [ABSTRACT FROM AUTHOR]

Recent spectral graph sparsification research aims to construct ultra-sparse subgraphs for preserving the original graph spectral (structural) properties, such as the first few Laplacian eigenvalues and eigenvectors, which has led to the development of a variety of nearly-linear time numerical and graph algorithms. However, there is very limited progress for spectral sparsification of directed graphs. In this work, we prove the existence of nearly-linear-sized spectral sparsifiers for directed graphs under certain conditions. Furthermore, we introduce a practically-efficient spectral algorithm (diGRASS) for sparsifying real-world, large-scale directed graphs leveraging spectral matrix perturbation analysis. The proposed method has been evaluated using a variety of directed graphs obtained from real-world applications, showing promising results for solving directed graph Laplacians, spectral partitioning of directed graphs, and approximately computing (personalized) PageRank vectors. [ABSTRACT FROM AUTHOR]

Motivated by the PageRank model for graph partitioning, we develop an extension of PageRank for partitioning uniform hypergraphs. Starting from adjacency tensors of uniform hypergraphs, we establish the multi-linear pseudo-PageRank (MLPPR) model, which is formulated as a multi-linear system with nonnegative constraints. The coefficient tensor of MLPPR is a kind of Laplacian tensors of uniform hypergraphs, which are almost as sparse as adjacency tensors since no dangling corrections are incorporated. Furthermore, all frontal slices of the coefficient tensor of MLPPR are M-matrices. Theoretically, MLPPR has a solution, which is unique under mild conditions. An error bound of the MLPPR solution is analyzed when the Laplacian tensor is slightly perturbed. Computationally, by exploiting the structural Laplacian tensor, we propose a tensor splitting algorithm, which converges linearly to a solution of MLPPR. Finally, numerical experiments illustrate that MLPPR is effective and efficient for hypergraph partitioning problems. [ABSTRACT FROM AUTHOR]

The Classifier Chains (CC) method is an effective method for multi-label classification, with its performance significantly contingent on the label order. However, most existing algorithms focus on determining the label order by considering label correlations as symmetric, ignoring that label correlations prevail asymmetric in real-world applications. To overcome the problem, we propose a novel Relative Entropy and PageRank-based Classifier Chains for multi-label classification (REPCC). Firstly, to completely capture label correlation, REPCC exploits relative entropy to estimate the asymmetric correlation between pairs of labels, filter out those pairs of labels with lower correlation, and construct a label correlation matrix. Secondly, by treating each label as a node, a directed graph is generated based on the filtered label correlation matrix. Finally, drawing an analogy between labels and web pages, the idea of PageRank, commonly used to measure the importance of web pages, is employed to rank labels in the directed graph and establish a classifier chain. Experimental results on ten public datasets from various domains show that REPCC outperforms state-of-the-art methods in CC.

Over the past few decades, the study of leadership theory has expanded across various disciplines, delving into the intricacies of human behavior and defining the roles of individuals within organizations. Its primary objective is to identify leaders who play significant roles in the communication flow. In addition, behavioral theory posits that leaders can be distinguished based on their daily conduct, while social network analysis provides valuable insights into behavioral patterns. Our study investigates five and six types of social networks frequently observed in different organizations. This study is conducted using datasets we collected from an IT company and public datasets collected from a manufacturing company for the thorough evaluation of prediction performance. We leverage PageRank and effective word embedding techniques to obtain novel features. State-of-the-art performance is obtained using various statistical machine learning methods, graph convolutional networks (GCN), automated machine learning (AutoML), and explainable artificial intelligence (XAI). More specifically, our approach can achieve state-of-the-art performance with an accuracy close to 90% for leaders identification with data from projects of different types. This investigation contributes to the establishment of sustainable leadership practices by aiding organizations in retaining their leadership talent.

Social media is a digital environment where users openly share their opinions and engage in debates and discussions on various topics. Social media has amassed an enormous quantity of accessible data due to its constantly expanding and highly active user base. This data is a valuable resource for researching a variety of topics. An example of such a research problem is identifying user similarities by analyzing their data. This article explores combining comments, textual posts, and likes for comments to create keywords or tags and group the users. These tags are extracted from user data and utilized in constructing a tag network. The tag network facilitates the formation of communities consisting of users with similar interests. User grouping is achieved based on the tags extracted from the posts. The proposed methods employ TF-IDF (term frequency-inverse document frequency) and TextRank algorithms to extract the tags. Kernel diffusion determines the similarities between tags in the tag network. Finally, an aggregation-hierarchical clustering algorithm is employed to group social media users based on these tags. [ABSTRACT FROM AUTHOR]

The article discusses a Bayesian measure of association, B-index, and compares it with the other existing measures of agreement, association, and similarity, both chance-corrected and non-corrected: Scott's π, Krippendorff's α, Cohen's κ, Bennett, Alpert & Goldstein's S, Cosine similarity, and the Jaccard similarity coefficient. PageRank adapted to particularities of annotation is also added to this list. Two versions of B-index are considered: with the informative and non-informative priors. An algorithm for calculating B-index written in pseudocode is provided. Particular attention is devoted to the uses of those measures in content analysis, communication studies, computational linguistics, psychology, computer science and network science. Real-world data gathered using an online platform for content analysis allowed comparing the behavior of all eight measures included in the scope of analysis. Three short texts (164 data points/sentences in total) were coded by 66 annotators. The behaviors of B-index with the non-informative prior and Bennett, Alpert & Goldstein's S have some common patterns. [ABSTRACT FROM AUTHOR]

Purpose – This study analyzed Korea's relations table through network analysis. In particular, among the centralities, eigenvector centrality, PageRank centrality and degree were used. The author studied which network characteristics affected the value-added rate. Design/methodology/approach – A network analysis method was used. Findings – It is the inward relationship that affects the value-added ratio of Korea's industries and the outward relationship has less influence. In particular, the inward relationship not only acts as a cost but also has an effect on the rate of added value recently. Research limitations/implications – Since the three years of 2010, 2015 and 2019 are the target, the data are somewhat insufficient to generalize. Practical implications – As for the value-added ratio of an industry, input is more important than output (sales). Therefore, where the input is received is very important. Social implications – It is possible to increase the understanding of the determinants of the value-added rate of Korean industries. Originality/value – (1) It was clarified which side is inward or outward in determining the industry in Korea. (2) The relationship between PageRank, eigenvector centrality and degree was analyzed in Korean cases. (3) Input is a cost and acts to increase added value.

Many ranking algorithms and metrics have been proposed to identify high-impact papers. Both the direct citation counts and the network-based PageRank-like algorithms are commonly used. Ideally, the more complete the data on the citation network, the more informative the ranking. However, obtaining more data on citation relations is often costly and challenging. In some cases, obtaining the citation counts can be relatively simple. In this paper, we look into using the additional citation counts but without additional citation relations to form more informative metrics for identifying high-impact papers. As an example, we propose enhancing the original PageRank algorithm by combining the local citation network with the additional citation counts from a more complete data source. We apply this enhanced method to American Physical Society (APS) papers to verify its effectiveness. The results indicate that the proposed ranking algorithm is robust against missing data and can improve the identification of high-quality papers. This shows that it is possible to enhance the effectiveness of a network-based metric calculated on a relatively small citation network by including only the additional data of the citation counts, without the additional citation relations.

One of the modern directions of obtaining information for making informed decisions is the analysis of data from open Internet sources, the analysis of media containing hundreds of thousands of publications. It is critically important not only to obtain reliable information, but also the time needed to obtain and analyze it. The purpose of the research in this work is the development and testing of a complex methodology for quickly building a prototype of a system for complex analysis of thematic sites. A technology of interconnected methods, methodologies, and tools for building a graph database, a knowledge graph, data analysis using methods and mo dels of machine learning with the provision of analytical results to users has been created. The main task of this work is to use these technologies to analyze data from well-known world sites in order to build a prototype of a systems for complex analysis of data from Internet sources.

Currently, high-performance computing environments are facing challenges such as limited resources and an increasing number of users. In order to improve the utilization of environmental resources, this paper proposes a high-performance hybrid computing architecture based on big data processing technology, which is constructed on the basis of an HDFS distributed system combined with MapReduce framework and GPU virtualization technology. The PageRank algorithm is utilized to evaluate the performance of rack nodes in the high-performance computing environment, and the evaluation results are applied to design an improvement strategy for task allocation and scheduling through the MapReduce framework. A division function is introduced to dynamically divide the Reduce data, and an approximate sampling method based on sampling information is proposed to guide the setting of the number of Reduce. The IB algorithm is used to cluster the labeled files, and a rack-aware strategy is designed based on HDFS to achieve resource load balancing. The MapReduce-based task allocation scheduling scheme has a reduction in job execution time of up to 39.83% compared to delayed scheduling. The dynamic partitioning design can achieve data load balancing by partitioning 5.382% of the groups and migrating 1.207% of the KVs if the data skew is 1.0. Dynamic balancing of environmental resources and resource scheduling optimization in high-performance computing environments can be achieved through the use of big data processing techniques.

In the digital era, the proliferation of new media, facilitated by digital platforms, has positioned new media marketing as a predominant marketing strategy. This study introduces a novel node ranking algorithm, leveraging the principles of PageRank, and proposes the UIEM-CMR influence assessment model derived from it. Utilizing data extracted from microblogging platforms and Facebook, this model evaluates the influence of various new media marketing nodes. Subsequently, the ten most influential nodes within the microblogging dataset were analyzed to examine the correlation between node influence in new media marketing and both the thematic distribution of new media marketing and the community distribution of followers. The findings reveal that the MR value of node influence, as calculated by the model, is impacted by the combined effects of AR and BR values, with these values alone identified as pivotal. The influence ranking, when solely based on AR and BR values, tends to diverge significantly from real-world scenarios. Nodes ranked highly under popular new media marketing topics exhibit a substantially greater probability of distribution compared to lower-ranked or randomly chosen nodes. Conversely, the distribution probabilities among influential nodes under less popular (cold) new media marketing topics show no significant disparity. Nodes with high influence ratings tend to attract followers from at least two distinct communities. The UIEM-CMR model, as developed in this study, proves to be an effective tool for assessing the influence of new media marketing strategies.

Creating policy measures is the final step in the process of e-learning roadmap development. Policy measures can be seen as long-term activities that need to be implemented and constantly upgraded to achieve strategic goals. For resource allocation, it is useful to prioritize policy measures. Prioritization can be implemented using multi-criteria decision-making methods. This paper analyzes policy measures in the Maldives National University's e-learning roadmap using the social network analysis process (SNAP), which includes the analytic hierarchy process (AHP), the decision-making trial and evaluation laboratory (DEMATEL), and the PageRank centrality. In policy measure evaluation, there were more than 20 participants: persons with managerial functions at the Maldives National University (MNU) (deans, heads of departments) and persons in lecturer and researcher positions. By using the AHP, participants prioritized policy measures with respect to their importance to them. By using the DEMATEL, participants identified and prioritized policy measures with respect to their effect on other measures. Finally, by using the SNAP, it was possible to determine the prioritization list for resource allocation since it aggregates the aspects of the policy measures, their importance, and their effect on other measures. [ABSTRACT FROM AUTHOR]

Back in 1988, the partial game trees explored by computer chess programs were among the largest search structures in real-world computing. Because the game tree is too large to be fully evaluated, chess programs must make heuristic strategic decisions based on partial information, making it an illustrative subject for teaching AI search. In one of his lectures that year on AI search for games and puzzles, Professor Hans Berliner—a pioneer of computer chess programs1—stated: "Intelligent heuristics are the future of computing." As a student in the field of the theory of computation, I was naturally perplexed but fascinated by this perspective. I had been trained to believe that "Algorithms and computational complexity theory are the foundation of computer science." However, as it happens, my attempts to understand heuristics in computing have subsequently played a significant role in my career as a theoretical computer scientist. I have come to realize that Berliner's postulation is a far-reaching worldview, particularly in the age of big, rich, complex, and multifaceted data and models, when computing has ubiquitous interactions with science, engineering, humanity, and society. In this article,2I will share some of my experiences on the subject of heuristics in computing, presenting examples of theoretical attempts to understand the behavior of heuristics on real data, as well as efforts to design practical heuristics with desirable theoretical characterizations. My hope is that these theoretical insights from past heuristics—such as spectral partitioning, multilevel methods, evolutionary algorithms, and simplex methods—can shed light on and further inspire a deeper understanding of the current and future techniques in AI and data mining. [ABSTRACT FROM AUTHOR]

There are errors in multi-source uncertain time series data. Truth discovery methods for time series data are effective in finding more accurate values, but some have limitations in their usability. To tackle this challenge, we propose a new and convenient truth discovery method to handle time series data. A more accurate sample is closer to the truth and, consequently, to other accurate samples. Because the mutual-confirm relationship between sensors is very similar to the mutual-quote relationship between web pages, we evaluate sensor reliability based on PageRank and then estimate the truth by sensor reliability. Therefore, this method does not rely on smoothness assumptions or prior knowledge of the data. Finally, we validate the effectiveness and efficiency of the proposed method on real-world and synthetic data sets, respectively. [ABSTRACT FROM AUTHOR]

Image sentiment analysis technology can predict, measure and understand the emotional experience of human beings through images. Aiming at the problem of extracting emotional characteristics in art appreciation, this article puts forward an innovative method. Firstly, the PageRank algorithm is enhanced using tweet content similarity and time factors; secondly, the SE-ResNet network design is used to integrate Efficient Channel Attention (ECA) with the residual network structure, and ResNeXt50 is optimized to enhance the extraction of image sentiment features. Finally, the weight coefficients of overall emotions are dynamically adjusted to select a specific emotion incorporation strategy, resulting in effective bimodal fusion. The proposed model demonstrates exceptional performance in predicting sentiment labels, with maximum classification accuracy reaching 88.20%. The accuracy improvement of 21.34% compared to the traditional deep convolutional neural networks (DCNN) model attests to the effectiveness of this study. This research enriches images and texts' emotion feature extraction capabilities and improves the accuracy of emotion fusion classification. [ABSTRACT FROM AUTHOR]

Ranking algorithms play a crucial role in information technologies and numerical analysis due to their efficiency in high dimensions and wide range of possible applications, including internet ranking, scientometrics, and systemic risk in finance (SinkRank and DebtRank). The traditional approach to internet ranking goes back to the seminal work of Sergey Brin and Larry Page, who developed the initial method PageRank (PR) in order to rank websites for search engine results based on linear algebra rules. But how robust is this method in times of rapid internet growth? Recent works have studied robust reformulations of the PageRank model for the case when links in the network structure may vary; that is, some links may appear or disappear, influencing the transportation matrix defined by the network structure. In this article, the authors make a further step forward, allowing the network to vary not only in links but also in the number of nodes. The authors focus on growing network structures and develop methods for ranking of networks uncertain both in size and in structure. Internet ranking algorithms play a crucial role in information technologies and numerical analysis due to their efficiency in high dimensions and wide range of possible applications, including scientometrics and systemic risk in finance (SinkRank, DebtRank, etc.). The traditional approach to internet ranking goes back to the seminal work of Sergey Brin and Larry Page, who developed the initial method PageRank (PR) in order to rank websites in search engine results. Recent works have studied robust reformulations of the PageRank model for the case when links in the network structure may vary; that is, some links may appear or disappear influencing the transportation matrix defined by the network structure. We make a further step forward, allowing the network to vary not only in links, but also in the number of nodes. We focus on growing network structures and propose a new robust formulation of the PageRank problem for uncertain networks with fixed growth rate. Defining the robust PageRank in terms of a nonconvex optimization problem, we bound our formulation from above by a convex but nonsmooth optimization problem. Driven by the approximation quality, we analyze the resulting optimality gap theoretically and demonstrate cases for its reduction. In the numerical part of the article, we propose some techniques which allow us to obtain the solution efficiently for middle-size networks avoiding all nonsmooth points. Furthermore, we propose a coordinate-wise descent method with near-optimal step size and address high-dimensional cases using multinomial transition probabilities. We analyze the impact of the network growth on ranking and numerically assess the approximation quality using real-world data sets on movie repositories and on journals on computational complexity. Funding: This work was supported by the Austrian Science Fund [Grant J3674-N26]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/opre.2022.2298. [ABSTRACT FROM AUTHOR]

Considering that travel agencies will pay attention to the demand information and electronic word-of-mouth(e-WOM)development trend of each alternative hotel when choosing partners, a method for hotel partner selection is proposed based on both information. First, the directed and weighted graph is constructed, in which the weight of the node is determined based on the demand information. Further, a time weight correction coefficient that considers the horizontal and vertical development trends of the alternatives’ e-WOM information is proposed to calculate the weight of each stage, and the TOWGA operator is used to calculate the attribute performance of each alternative hotel. By comparing the e-WOM attribute performance, the directed edge and its weight are determined. Then, the algorithm for calculating the ranking value of the alternative hotels is given based on PageRank. Finally, an example is used to verify the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

We consider several families of network centrality measures induced by graph kernels, which include some well-known measures and many new ones. The Self-consistency and Bridge axioms, which appeared earlier in the literature, are closely related to certain kernels and one of the families. We obtain a necessary and sufficient condition for Self-consistency, a sufficient condition for the Bridge axiom, indicate specific measures that satisfy these axioms and show that under some additional conditions they are incompatible. PageRank centrality applied to undirected networks violates most conditions under study and has a property that according to some authors is 'hard to imagine' for a centrality measure. We explain this phenomenon. Adopting the Self-consistency or Bridge axiom leads to a drastic reduction in survey time in the culling method designed to select the most appropriate centrality measures. [ABSTRACT FROM AUTHOR]

In the field of deep matching, a large amount of noisy data in Chinese long texts affects the matching effect. Most long-form text matching models use all text data indiscriminately, which results in a large amount of noisy data, and thus the PageRank algorithm is combined with Transformer to filter noise. For sentence-level noise detection, after calculating the overlap rate of words to evaluate the similarity, a sentence-level relationship graph is constructed and filtered by using the PageRank algorithm; for word-level noise detection, based on the attention score in Transformer, a word graph is established, then the PageRank algorithm is executed on graph, combined with self-attention weights, to select keywords to highlight topic relevance, the noisy words are filtered sequentially at different layers in the module, layer by layer. In addition, during the model training, PolyLoss is applied to replace the traditional binary Cross-Entropy loss function, thus reducing the difficulty of hyperparameter tuning. Finally, a better filtering strategy is proposed and experiments are conducted to verify it on two Chinese long-form text matching datasets. The result shows that the matching model based on the noise filtering strategy of this paper can better filter the noise and capture the matching signal more accurately. [ABSTRACT FROM AUTHOR]

Climate risk is one of the key factors affecting the energy industry, and then affects the global energy trade. By adopting a network approach to the global energy trade and based on the data of global energy trade during 1996–2018, the evolution characteristics of the global energy trade network are hereby investigated, and the impact of climate risk on the global energy trade is explored. The results demonstrate that the global energy trade network has scale-free characteristics and significant regional characteristics and that its heterogeneity is weakening on the whole. Climate risk is negatively correlated with the energy trade relationship between countries. The simulation results show that climate risk has a greater impact on the import status of all countries than their export status and that only a few countries have large climate risk spillover effects. In addition, the climate risk spillover effects of countries are closely related to their import statuses. Overall, the present study provides policy suggestions for identifying important energy trading countries under climate risk shock and preventing the negative impact of climate risks on global energy trade. [ABSTRACT FROM AUTHOR]

In this study, we explore how transformer models, which are known for their attention mechanisms, can improve pathogen prediction in pastured poultry farming. By combining farm management practices with microbiome data, our model outperforms traditional prediction methods in terms of the F1 score—an evaluation metric for model performance—thus fulfilling an essential need in predictive microbiology. Additionally, the emphasis is on making our model’s predictions explainable. We introduce a novel approach for identifying feature importance using the model’s attention matrix and the PageRank algorithm, offering insights that enhance our comprehension of established techniques such as DeepLIFT. Our results showcase the efficacy of transformer models in pathogen prediction for food safety and mark a noteworthy contribution to the progress of explainable AI within the biomedical sciences. This study sheds light on the impact of effective farm management practices and highlights the importance of technological advancements in ensuring food safety.

In this paper, a new multi-parameter iterative algorithm is proposed to address the PageRank problem based on the multi-splitting iteration method. The proposed method solves two linear subsystems at each iteration by splitting the coefficient matrix, considering therefore inner and outer iteration to find the approximate solutions of these linear subsystems. It can be shown that the iterative sequence generated by the multi-parameter iterative algorithm finally converges to the PageRank vector when the parameters satisfy certain conditions. Numerical experiments show that the proposed algorithm has better convergence and numerical stability than the existing algorithms. [ABSTRACT FROM AUTHOR]

PageRank is a popular measure of centrality that is often applied to rank nodes in real-world networks. However, in many cases, the notion of teleportation is counterintuitive because it implies that whatever is moving around the network will jump or 'teleport' directly from one node to any other, without considering how far apart the nodes are. To overcome this issue, we propose here a general measure of PageRank centrality whereby the teleportation probabilities depend, in some way, on the distance separating the nodes. We accomplish this by drawing upon recent advances in non-local random walks, which allow the proposed measure to be tailored for various real-world networks and applications. To illustrate the flexibility of the proposed measure and to demonstrate how it differs from PageRank centrality, we present and discuss experimental results for a selection of real-world spatial and social networks, including an air transportation network, a collaboration network and an urban street network. [ABSTRACT FROM AUTHOR]

Abstract We propose a greedy separation algorithm that finds the most fitted candidate among stochastic block models for a network, based on three known approaches. The first approach tests whether the network has one or more than two communities based on the distribution of the largest eigenvalue of the adjacency matrix. The second uses the algorithm to infer the classed label of each node in the network. The third approach ascertains the optimal number of clusters using an information criterion based on the Bayesian information criterion. The algorithm combined with the above approaches can find a suitable candidate from successively generated stochastic block models. However, in the second approach, the estimated labels heavily depend on the initial labels. The collection of the hub node and its neighbors is expected to construct one class with the same label. We find the hub nodes and enhance the initial labels by the PageRank method. We also conduct experiments with real data to evaluate the accuracy of the proposed method by comparing it with Markov chain Monte Carlo methods. The greedy separation algorithm with the PageRank method is preferable to the Monte Carlo-based methods. [ABSTRACT FROM AUTHOR]

Search engine optimization (SEO), the practice of improving website visibility on search engines, faces the considerable challenges posed by the opacity of Google’s relevance ranking algorithm. Attempts at understanding how this algorithm operates have generated a sizeable number of studies in the worlds of both business and academia. Indeed, this research tradition has managed to present strong evidence regarding the participation of certain factors and their relative importance. For instance, there is a widespread consensus that domain authority is one of the key factors in optimizing positioning. This study seeks to determine the reliability of the domain authority scores provided by three leading platforms for SEO professionals: Moz’s Domain Authority, Semrush’s Authority Score, and Ahrefs’ Domain Rating, values obtained using different indices and applying different procedures. We hypothesize that the degree of coincidence is high, allowing us to deduce that the three tools are, therefore, highly reliable. The method of data triangulation is used to compare the values from these three sources. The degree of coincidence is determined using a statistical analysis based on Spearman’s correlation coefficient (rho). The sample of domains analyzed was selected from 61 neutral queries, which provided 16,937 results and a total of 3,151 domains. When examining the tools in pairs, the correlation coefficients obtained were above 0.9 in all cases. The rho coefficient of the global analysis was also 0.9. This confirms our hypothesis and demonstrates that the three platforms can be considered as providing reliable data. These results are clearly relevant given that SEO professionals depend heavily on domain authority values in their work, and the degree of reliability detected ensures that decision-making based on this indicator can be undertaken with confidence. [ABSTRACT FROM AUTHOR]

We study the complexity of local graph-centrality estimation, with the goal of approximating the centrality score of a given target node while exploring only a sublinear number of nodes/arcs of the graph and performing a sublinear number of elementary operations. We develop a technique, which we apply to PageRank and Heat Kernel, for constructing a low-variance score estimator through a local exploration of the graph. We obtain an algorithm that, given any node in any graph of n nodes and m arcs, with probability (1 - \delta ) computes a multiplicative (1\pm \epsilon )-approximation of its score by examining only O\~(min(n 1/2\Delta 1/2 , n 1/2m1/4 )) nodes/arcs, where \Delta is the maximum outdegree of the graph and poly(\epsilon - 1 ) and polylog(\delta - 1 ) factors are omitted for readability. A similar bound holds for computational cost. We also prove a lower bound of \Omega (min(n 1/2\Delta 1/2, n1/3m1/3 )) for both query complexity and computational complexity. Moreover, in the jump-and-crawl graphaccess model, our technique yields a O\~(min(n 1/2\Delta 1/2 , n2/3 ))-queries algorithm; we show that this algorithm is optimal up to a logarithmic factor-in fact, sublogarithmic in the case of PageRank. These are the first algorithms with sublinear worst-case bounds for general directed graphs and any choice of the target node. [ABSTRACT FROM AUTHOR]

Consider chemical elements as a system, we create an undirected chemical network with 99 elements and 1916 edges from Chemspider, a website that provide search engines to collect compounds. Using this network and the network that we used in our previous work with 97 elements and 2198 edges, we found that RootedPageRank, a link prediction tool in complex network, can be used to predict potential binary compounds, because the changing trend of PageRank probability of each element in these networks all follow the periodic law, despite of the difference of scale of these networks. The accuracy test indicates that at least 7 among top 10 predicted compoundss in one network can be verified using the compoundss in the other network or in other chemical database, proving that this method can be used to provide guidance in finding potential binary compounds, suggesting that we can study chemical properties from the view of complex network. [ABSTRACT FROM AUTHOR]