PageRank Research Articles

KGRLFF: Detecting Drug-Drug Interactions Based on Knowledge Graph Representation Learning and Feature Fusion.

Accurate prediction of drug-drug interactions (DDIs) plays an important role in improving the efficiency of drug development and ensuring the safety of combination therapy. Most existing models rely on a single source of information to predict DDIs, and few models can perform tasks on biomedical knowledge graphs. This paper proposes a new hybrid method, namely Knowledge Graph Representation Learning and Feature Fusion (KGRLFF), to fully exploit the information from the biomedical knowledge graph and molecular structure of drugs to better predict DDIs. KGRLFF first uses a Bidirectional Random Walk sampling method based on the PageRank algorithm (BRWP) to obtain higher-order neighborhood information of drugs in the knowledge graph, including neighboring nodes, semantic relations, and higher-order information associated with triple facts. Then, an embedded representation learning model named Knowledge Graph-based Cyclic Recursive Aggregation (KGCRA) is used to learn the embedded representations of drugs by recursively propagating and aggregating messages with drugs as both the source and destination. In addition, the model learns the molecular structures of the drugs to obtain the structured features. Finally, a Feature Representation Fusion Strategy (FRFS) was developed to integrate embedded representations and structured feature representations. Experimental results showed that KGRLFF is feasible for predicting potential DDIs.

Enhancing Village Ranking: Leveraging Cluster Analysis and Machine Learning

The research presented in this paper addresses the critical challenge of ranking Indian villages based on their potential for socio-economic growth, inspired by the objectives of the Shyam Prasad Mukerji Rurban Mission (SPMRM). Leveraging a novel algorithm known as ClusterRank, an extension of the PageRank algorithm, we endeavor to provide a timely, efficient, and accurate solution for village ranking. The ClusterRank algorithm, an extension of the well-known PageRank algorithm, serves as the cornerstone of our approach. It excels in its ability to efficiently and accurately rank villages, outperforming alternative ranking methods. Notably, our findings reveal the superior convergence speed of the ClusterRank algorithm, affirming its capacity to produce rapid results. To assess the efficacy of ClusterRank, we employ a range of statistical ranking coefficients, including the Spearman, Kendall, and Point Biserial correlation coefficients. Our analysis demonstrates a strong positive correlation, with a Spearman correlation coefficient of 0.89, between the rankings generated by ClusterRank and the ground truth rankings provided by SPMRM. This correlation underscores the effectiveness of ClusterRank in identifying villages with high growth potential and highlights its alignment with the objectives of the SPMRM. This research presents a compelling case for the adoption of the ClusterRank algorithm by SPMRM as a valuable tool in the identification of rural areas poised for rurban development. Such adoption not only promises substantial time and resource savings but also directs the focus of SPMRM towards the implementation of targeted interventions and initiatives to uplift rural communities. Moreover, our supplementary analyses using Kendall and Point Biserial correlation coefficients further validate the robustness of the ClusterRank algorithm.

Key sector analysis by IO networks: Evidence from Turkey

In this study, the key sectors of Turkey are determined using Input- Output (IO) network analysis. Different centrality measures are analyzed and compared. Accordingly, eigenvector, page rank, hub, and authority centralities are calculated. The effects of an economic shock are also analyzed using random walk centrality and counting betweenness. Findings indicate that the key sectors are ?Wholesale and Retail Trade; Repair of Motor Vehicles?, ?Transport, Storage and Communications?, ?Agriculture, Hunting and Forestry?, ?Construction?, ?Real Estate Activities?, and ?Textiles, Textile Products, Leather and Footwear?. Furthermore, the same sectors are determined to be vulnerable to economic shocks.

Alleviating Cold Start in the EOSC Recommendations: Extended Page Rank Algorithm

Alleviating Cold Start in the EOSC Recommendations: Extended Page Rank Algorithm

Classification and Informatization of Subject Resources for English Language Teaching in the Context of Data Theory

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

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.

GPPR: Geo-distributed Personalized PageRank Algorithm

GPPR: Geo-distributed Personalized PageRank Algorithm

A Subgraph Retrieval Method for Complex Questions Based on Hybrid Semantics and Path Representation

Current subgraph retrieval methods generally fall into two categories: those that rely on semantic matching, which use only surface-level semantic information of relations and lack flexibility; and those based on personalized PageRank algorithms, which fail to leverage the semantic connection between the relation and the question, rendering them susceptible to noisy data. To address these issues, this paper introduces a novel retrieval model that employs hybrid semantics of relations and path representations. Specifically, hybrid semantics involves merging relational and entity information within a knowledge graph to extract the deep semantics of relations and enhance semantic representation by integrating it with the explicit descriptive text of the relations. Path representation merges the semantics of the current relation with those of preceding ones to form a complete path representation. This representation is then semantically matched with the question to compute a score, which determines whether the relation should form part of the subgraph. We integrated our subgraph retrieval model with the Neural Symbolic Machine (NSM) reasoning model and evaluated it on the publicly available CWQ and WebQSP datasets. The experimental results demonstrate that our method performs exceptionally well on these datasets, validating the efficacy of utilizing deep semantics and path representations for the retrieval of subgraphs in response to complex questions.

Deep Semi-Supervised Learning Method for False Data Detection Against Forgery and Concealing of Faults in Cyber-Physical Power Systems

This paper proposes a deep semi-supervised numerical false data detection (DSS-NFDD) method, where hidden data in labeled and unlabeled datasets are leveraged simultaneously, to detect the false data in CPPS. Two types of false data are considered in this study: one is the forged fault data, which makes operators mistakenly believe that there is a fault in their operating system; the other is the false data used to conceal actual faults. First, a data dimension reduction method is proposed based on the PageRank algorithm to avoid the excessive noise caused by high-dimension data. Then, a semi-supervised deep learning framework is established to detect false data samples, which consists of two parts: one is the priori estimation module, and the other is a false data scoring network. A novel concentration loss function is presented for training the false data scoring network, which minimizes the impacts of noise pollution and sample bias. Next, a false feature location method is proposed to help human operators eliminate anomalies. Finally, the effectiveness and the superiorities of the proposed DSS-NFDD method are verified by analyzing the simulation results for the IEEE-39 bus and IEEE-118 bus systems.

Tokopedia Digital Engagement Monitoring through Trend Analytics: Case Study of Indonesia

Purchases are initiated on the Tokopedia platform by more than 120 million monthly users. There is a 43% decline in the prominence of Tokopedia between 2020 and 2023. The decrease in search engine traffic on Google has led to this outcome. Their comprehensive advertising strategy, which combines traditional and digital marketing methods, is probably the reason for Tokopedia’s high Google ranking. The reversal of the declining trend line and its subsequent ascent are anticipated outcomes of the cross-average analysis during the upcoming semester. Nevertheless, attaining the identical degree of recognition as in 2020 will prove difficult on account of the limited volume. Decreased efficacy or communication intensity could contribute to a decline in the prevalence of digital media. Spiking interest among the target market and refining communication strategies could potentially improve this. To analyze the pertinent market volume and popularity trends for exposure, additional research is required.

Exploring the Role of Artificial Intelligence in Multimodal Cultural Communication and Identification

Abstract Artificial intelligence technology has unique advantages in processing multimodal information such as images, videos and sounds, and plays an important role in the process of enhancing the effect of multimodal cultural communication. The Page Rank model is used to identify communication subjects based on the role of key communication subjects in cultural communication, as proposed in this paper. A genetic algorithm is utilized to improve the traditional neural network algorithm, so that it provides more accurate prediction results and countermeasures in the process of promoting cultural communication. The analysis shows that the OLMT algorithm has an average accuracy of 94.56% in a specific identification interval. The difference between the expected and actual output values of the improved neural network algorithm for the test samples was less than 0.7.

Linear Kernel Pattern Matched Discriminative Deep Convolutive Neural Network for Dynamic Web Page Ranking with Big Data

Linear Kernel Pattern Matched Discriminative Deep Convolutive Neural Network for Dynamic Web Page Ranking with Big Data

Linear Kernel pattern matched discriminative deep convolutive neural network for dynamic web page ranking with big data

Linear Kernel pattern matched discriminative deep convolutive neural network for dynamic web page ranking with big data

Identification of Critical Nodes in Power Grid Based on Improved PageRank Algorithm and Power Flow Transfer Entropy

Identifying critical nodes in the power grid is a crucial aspect of power system security and stability analysis. However, the current methods for identification fall short in fully accounting for the power transfer characteristics between nodes and the consequences of node removal on the security and stability of power grid operation. To enhance the effective and accurate identification of critical nodes in the power grid, a method is proposed. This method is based on improved PageRank algorithm and node-weighted power flow transfer entropy, referred to as IPRA-PFTE. Firstly, based on the power flow and equivalent impedance between nodes, and the introduction of virtual nodes, an improved PageRank algorithm is obtained. Then the node-weighted power flow transfer entropy is derived by considering the uniformity of the transfer power flow distribution in the system following the removal of a node. Finally, the importance of nodes is obtained by combining the improved PageRank algorithm with the node-weighted power flow transfer entropy. The method’s effectiveness and accuracy are validated through simulation using the IEEE 39-bus example and subsequent comparison with existing methods.

Optimizing the Efficiency of Web Mining through Comparative Web Ranking Algorithms

Optimizing the Efficiency of Web Mining through Comparative Web Ranking Algorithms

AMFC: A New Approach Efficient Junctions Detect via Maximum Flow Approach

In this study, max flow analysis processes are carried out with a graph theory-based approach that can be used in optimizing the traffic load in transportation networks. The data used in the study consists of 2 years of vehicle number data consisting of 438 million vehicle passes of a real city. Bottleneck points affecting traffic flow, maximum flow values, and effectiveness values of traffic generating and attracting locations were determined in the uniquely created transportation network. The Ford-Fulkerson algorithm was used to determine the maximum flow and bottleneck road connections in the designed transportation network. According to the maximum traffic flow to the junction point, the most effective junction points were determined by the PageRank algorithm. In addition, a unique algorithm is presented in the study that determines the effective intersection points that transfer vehicle traffic at maximum capacity to all junction points according to the maximum demand capacity data.

Product color emotional design based on 3D knowledge graph

To address the problem of fragmentation, integration difficulties in fuzzy front-end information, and ambiguity in color emotion knowledge representation and conversion within the current product color emotion design stage, this paper proposes a method based on 3D Knowledge Graph. The proposed approach aims to integrate product color emotion design into the “data knowledge + artificial intelligence” growth model, facilitating a beneficial knowledge output cycle driven by data. As for the proposed approach, it divides the design problem into three stages: in the first one, big data web crawler technology and natural language processing were employed to extract knowledge related to product color emotion design. In the second phase, the construction of a product color emotion imagery association model using the RotatE knowledge graph, thereby achieving knowledge fusion of product color emotion design, and all accumulated knowledge data is integrated into a 3D Knowledge Graph for visualization. Finally, in the third phase, the PageRank algorithm is applied to calculate primary and auxiliary color weight parameters, simulating the product color synergy mechanism and determining the color synergy effects. Then, realize the knowledge generation of product color emotional design. This approach combines the human visual experience feedback with extensive big data analysis, and accurately outputs the product color emotion design scheme that meets the user's emotional needs. Moreover, the effectiveness and applicability of the method are verified by an illustrative example involving modern machine tool.

Art appreciation model design based on improved PageRank and ECA-ResNeXt50 algorithm.

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.

Large-scale effective connectivity analysis reveals the existence of two mutual inhibitory systems in patients with major depression

It is posited that cognitive and affective dysfunction in patients with major depression disorder (MDD) may be caused by dysfunctional signal propagation in the brain. By leveraging dynamic causal modeling, we investigated large-scale directed signal propagation (effective connectivity) among distributed large-scale brain networks with 43 MDD patients and 56 healthy controls. The results revealed the existence of two mutual inhibitory systems: the anterior default mode network, auditory network, sensorimotor network, salience network and visual networks formed an “emotional” brain, while the posterior default mode network, central executive networks, cerebellum and dorsal attention network formed a “rational brain”. These two networks exhibited excitatory intra-system connectivity and inhibitory inter-system connectivity. Patients were characterized by potentiated intra-system connections within the “emotional/sensory brain”, as well as over-inhibition of the “rational brain” by the “emotional/sensory brain”. The hierarchical architecture of the large-scale effective connectivity networks was then analyzed using a PageRank algorithm which revealed a shift of the controlling role of the “rational brain” to the “emotional/sensory brain” in the patients. These findings inform basic organization of distributed large-scale brain networks and furnish a better characterization of the neural mechanisms of depression, which may facilitate effective treatment.

Comprehensive assessment of cable-stayed bridge based on Pagerank algorithm

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