Big Data最新文献

{"title":"A Study of Public Opinion Reversal Recognition of Emergency Based on Hypernetwork.","authors":"Xuna Wang","doi":"10.1177/2167647X251366060","DOIUrl":"https://doi.org/10.1177/2167647X251366060","url":null,"abstract":"<p><p>With the rapid development of social media and online platforms, the speed and influence of emergency dissemination in cyberspace have significantly increased. The swift changes in public opinion, especially the phenomenon of opinion reversals, exert profound impacts on social stability and government credibility. The hypernetwork structure, characterized by its multilayered and multidimensional complexity, offers a new theoretical framework for analyzing multiagents and their interactions in the evolution of public opinion. Based on hypernetwork theory, this study constructs a four-layer subnet model encompassing user interaction network, event evolution network, semantic association network, and emotional conduction network. By extracting network structural features and conducting cross-layer linkage analysis, an identification system for public opinion reversals in emergencies is established. Taking the donation incident involving Hongxing Erke during the Henan rainstorm in 2021 as a case study, an empirical analysis of the public opinion reversal process is conducted. The research results indicate that the proposed hypernetwork model can effectively identify key nodes in public opinion reversals. The multi-indicator collaborative identification system for public opinion reversals aids in rapidly and effectively detecting signals of such reversals. This study not only provides new methodological support for the dynamic identification of public opinion reversals but also offers theoretical references and practical guidance for public opinion monitoring and emergency response decision-making in emergencies.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Fast Survival Support Vector Regression Approach to Large Scale Credit Scoring via Safe Screening.","authors":"Hong Wang, Ling Hong","doi":"10.1089/big.2023.0033","DOIUrl":"10.1089/big.2023.0033","url":null,"abstract":"<p><p>Survival models have found wider and wider applications in credit scoring recently due to their ability to estimate the dynamics of risk over time. In this research, we propose a Buckley-James safe sample screening support vector regression (BJS4VR) algorithm to model large-scale survival data by combing the Buckley-James transformation and support vector regression. Different from previous support vector regression survival models, censored samples here are imputed using a censoring unbiased Buckley-James estimator. Safe sample screening is then applied to discard samples that guaranteed to be non-active at the final optimal solution from the original data to improve efficiency. Experimental results on the large-scale real lending club loan data have shown that the proposed BJS4VR model outperforms existing popular survival models such as RSFM, CoxRidge and CoxBoost in terms of both prediction accuracy and time efficiency. Important variables highly correlated with credit risk are also identified with the proposed method.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"304-318"},"PeriodicalIF":2.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-Path Graph Neural Network with Adaptive Auxiliary Module for Link Prediction.","authors":"Zhenzhen Yang, Zelong Lin, Yongpeng Yang, Jiaqi Li","doi":"10.1089/big.2023.0130","DOIUrl":"10.1089/big.2023.0130","url":null,"abstract":"<p><p>Link prediction, which has important applications in many fields, predicts the possibility of the link between two nodes in a graph. Link prediction based on Graph Neural Network (GNN) obtains node representation and graph structure through GNN, which has attracted a growing amount of attention recently. However, the existing GNN-based link prediction approaches possess some shortcomings. On the one hand, because a graph contains different types of nodes, it leads to a great challenge for aggregating information and learning node representation from its neighbor nodes. On the other hand, the attention mechanism has been an effect instrument for enhancing the link prediction performance. However, the traditional attention mechanism is always monotonic for query nodes, which limits its influence on link prediction. To address these two problems, a Dual-Path Graph Neural Network (DPGNN) for link prediction is proposed in this study. First, we propose a novel Local Random Features Augmentation for Graph Convolution Network as a baseline of one path. Meanwhile, Graph Attention Network version 2 based on dynamic attention mechanism is adopted as a baseline of the other path. And then, we capture more meaningful node representation and more accurate link features by concatenating the information of these two paths. In addition, we propose an adaptive auxiliary module for better balancing the weight of auxiliary tasks, which brings more benefit to link prediction. Finally, extensive experiments verify the effectiveness and superiority of our proposed DPGNN for link prediction.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"333-343"},"PeriodicalIF":2.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140289590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Content-Aware Human Mobility Pattern Extraction.","authors":"Shengwen Li, Chaofan Fan, Tianci Li, Renyao Chen, Qingyuan Liu, Junfang Gong","doi":"10.1089/big.2022.0281","DOIUrl":"10.1089/big.2022.0281","url":null,"abstract":"<p><p>Extracting meaningful patterns of human mobility from accumulating trajectories is essential for understanding human behavior. However, previous works identify human mobility patterns based on the spatial co-occurrence of trajectories, which ignores the effect of activity content, leaving challenges in effectively extracting and understanding patterns. To bridge this gap, this study incorporates the activity content of trajectories to extract human mobility patterns, and proposes acontent-aware mobility pattern model. The model first embeds the activity content in distributed continuous vector space by taking point-of-interest as an agent and then extracts representative and interpretable mobility patterns from human trajectory sets using a derived topic model. To investigate the performance of the proposed model, several evaluation metrics are developed, including pattern coherence, pattern similarity, and manual scoring. A real-world case study is conducted, and its experimental results show that the proposed model improves interpretability and helps to understand mobility patterns. This study provides not only a novel solution and several evaluation metrics for human mobility patterns but also a method reference for fusing content semantics of human activities for trajectory analysis and mining.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"269-284"},"PeriodicalIF":2.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141565068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on the Influence of Information Iterative Propagation on Complex Network Structure.","authors":"Yinuo Qian, Fuzhong Nian, Zheming Wang, Yabing Yao","doi":"10.1089/big.2023.0016","DOIUrl":"10.1089/big.2023.0016","url":null,"abstract":"<p><p>Dynamic propagation will affect the change of network structure. Different networks are affected by the iterative propagation of information to different degrees. The iterative propagation of information in the network changes the connection strength of the chain edge between nodes. Most studies on temporal networks build networks based on time characteristics, and the iterative propagation of information in the network can also reflect the time characteristics of network evolution. The change of network structure is a macromanifestation of time characteristics, whereas the dynamics in the network is a micromanifestation of time characteristics. How to concretely visualize the change of network structure influenced by the characteristics of propagation dynamics has become the focus of this article. The appearance of chain edge is the micro change of network structure, and the division of community is the macro change of network structure. Based on this, the node participation is proposed to quantify the influence of different users on the information propagation in the network, and it is simulated in different types of networks. By analyzing the iterative propagation of information, the weighted network of different networks based on the iterative propagation of information is constructed. Finally, the chain edge and community division in the network are analyzed to achieve the purpose of quantifying the influence of network propagation on complex network structure.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"319-332"},"PeriodicalIF":2.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141789804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Multilayer Networks Through Time-Dependent Decision-Making: A Comparative Study.","authors":"Kenan Menguc, Alper Yilmaz","doi":"10.1089/big.2024.0094","DOIUrl":"https://doi.org/10.1089/big.2024.0094","url":null,"abstract":"<p><p>This research highlights the importance of accurately analyzing real-world multilayer network problems and introduces effective solutions. Whether simulating protein-protein network, transportation network, or a social network, representation and analysis over these networks are crucial. Multilayer networks, that contain added layers, may undergo dynamic transformations over time akin to single-layer networks that experience changes over time. These dynamic networks, that expand and contract, can be optimized by guidance from human operators if the transient changes are known and can be controlled. For the expansion and contraction of networks, this study introduces two distinct algorithms designed to make optimal decisions across dynamic changes of a multilayer network. The main strategy is to minimize the standard deviation across betweenness centrality of the edges in a complex network. The approaches we introduce incorporate diverse constraints into a multilayer weighted network, probing the network's expansion or contraction under various conditions represented as objective functions. The addition of changing of objective function enhances the model's adaptability to solve a wide array of problem types. In this way, complex network structures representing real-world problems can be mathematically modeled which makes it easier to make informed decisions.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144585578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Based Decision Support System for Nurse Staff in Hospitals.","authors":"Jieyu Chen, Feilong He, Lihua Tang, Lingli Gu","doi":"10.1089/big.2024.0122","DOIUrl":"https://doi.org/10.1089/big.2024.0122","url":null,"abstract":"<p><p>To promote the informatization management of hospital human resources and advance the application of hospital information technology. The application of deep learning (DL) technologies in health care, particularly in hospital settings, has shown significant promise in enhancing decision-making processes for nurse staff. Utilizing a hospital management decision support system based on data warehouse theory and business intelligence technology to achieve multidimensional analysis and display of data. This research explores the development and implementation of a DL-Based Clinical Decision Support System (DL-CDSS) tailored for nurses in hospitals. DL-CDSS utilizes advanced neural network architectures to analyze complex clinical data, including patient records, vital signs, and diagnostic reports, aiming to assist nurses in making informed decisions regarding patient care. By leveraging large-scale datasets from Hospital Information Systems, DL-CDSS provides real-time recommendations for treatment plans, medication administration, and patient monitoring. The system's effectiveness is demonstrated through improved accuracy in clinical decision-making, reduction in medication errors, and optimized workflow efficiency. The system analyzes and displays nurses data from hospitals in terms of quantity, distribution, structure, forecasting, analysis reports, and peer comparisons, providing head nurses with multilevel, multiperspective data mining analysis results. Challenges such as data integration, model interpretability, and user interface design are addressed to ensure seamless integration into nursing practice, also concludes with insights into the potential benefits of DL-CDSS in promoting patient safety, enhancing health care quality, and supporting nursing professionals in delivering optimal care.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Impact of the COVID-19 Pandemic on Stock Market Performance in G20 Countries: Evidence from Long Short-Term Memory with a Recurrent Neural Network Approach.","authors":"Pingkan Mayosi Fitriana, Jumadil Saputra, Zairihan Abdul Halim","doi":"10.1089/big.2023.0015","DOIUrl":"10.1089/big.2023.0015","url":null,"abstract":"<p><p>In light of developing and industrialized nations, the G20 economies account for a whopping two-thirds of the world's population and are the largest economies globally. Public emergencies have occasionally arisen due to the rapid spread of COVID-19 globally, impacting many people's lives, especially in G20 countries. Thus, this study is written to investigate the impact of the COVID-19 pandemic on stock market performance in G20 countries. This study uses daily stock market data of G20 countries from January 1, 2019 to June 30, 2020. The stock market data were divided into G7 countries and non-G7 countries. The data were analyzed using Long Short-Term Memory with a Recurrent Neural Network (LSTM-RNN) approach. The result indicated a gap between the actual stock market index and a forecasted time series that would have happened without COVID-19. Owing to movement restrictions, this study found that stock markets in six countries, including Argentina, China, South Africa, Turkey, Saudi Arabia, and the United States, are affected negatively. Besides that, movement restrictions in the G7 countries, excluding the United States, and the non-G20 countries, excluding Argentina, China, South Africa, Turkey, and Saudi, significantly impact the stock market performance. Generally, LSTM prediction estimates relative terms, except for stock market performance in the United Kingdom, the Republic of Korea, South Africa, and Spain. The stock market performance in the United Kingdom and Spain countries has significantly reduced during and after the occurrence of COVID-19. It indicates that the COVID-19 pandemic considerably influenced the stock markets of 14 G20 countries, whereas less severely impacting 6 remaining countries. In conclusion, our empirical evidence showed that the pandemic had restricted effects on the stock market performance in G20 countries.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"219-242"},"PeriodicalIF":2.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138832891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investment Recommender System Model Based on the Potential Investors' Key Decision Factors.","authors":"Asefeh Asemi, Adeleh Asemi, Andrea Ko","doi":"10.1089/big.2022.0302","DOIUrl":"10.1089/big.2022.0302","url":null,"abstract":"<p><p>In this research, we propose an automatic recommender system for providing investment-type suggestions offered to investors. This system is based on a new intelligent approach using an adaptive neuro-fuzzy inference system (ANFIS) that works with four potential investors' key decision factors (KDFs), which are system value, environmental awareness factors, the expectation of high return, and expectation of low return. The proposed system provides a new model for investment recommender systems (IRSs), which is based on the data of KDFs, and the data related to the type of investment. The solution of fuzzy neural inference and choosing the type of investment is used to provide advice and support the investor's decision. This system also works with incomplete data. It is also possible to apply expert opinions based on feedback provided by investors who use the system. The proposed system is a reliable system for providing suggestions for the type of investment. It can predict the investors' investment decisions based on their KDFs in the selection of different investment types. This system uses the K-means technique in JMP for preprocessing the data and ANFIS for evaluating the data. We also compare the proposed system with other existing IRSs and evaluate the system's accuracy and effectiveness using the root mean squared error method. Overall, the proposed system is an effective and reliable IRS that can be used by potential investors to make better investment decisions.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"197-218"},"PeriodicalIF":2.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9432264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling of Machine Learning-Based Extreme Value Theory in Stock Investment Risk Prediction: A Systematic Literature Review.","authors":"Melina Melina, Sukono, Herlina Napitupulu, Norizan Mohamed","doi":"10.1089/big.2023.0004","DOIUrl":"10.1089/big.2023.0004","url":null,"abstract":"<p><p>The stock market is heavily influenced by global sentiment, which is full of uncertainty and is characterized by extreme values and linear and nonlinear variables. High-frequency data generally refer to data that are collected at a very fast rate based on days, hours, minutes, and even seconds. Stock prices fluctuate rapidly and even at extremes along with changes in the variables that affect stock fluctuations. Research on investment risk estimation in the stock market that can identify extreme values is nonlinear, reliable in multivariate cases, and uses high-frequency data that are very important. The extreme value theory (EVT) approach can detect extreme values. This method is reliable in univariate cases and very complicated in multivariate cases. The purpose of this research was to collect, characterize, and analyze the investment risk estimation literature to identify research gaps. The literature used was selected by applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and sourced from Sciencedirect.com and Scopus databases. A total of 1107 articles were produced from the search at the identification stage, reduced to 236 in the eligibility stage, and 90 articles in the included studies set. The bibliometric networks were visualized using the VOSviewer software, and the main keyword used as the search criteria is \"VaR.\" The visualization showed that EVT, the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, and historical simulation are models often used to estimate the investment risk; the application of the machine learning (ML)-based investment risk estimation model is low. There has been no research using a combination of EVT and ML to estimate the investment risk. The results showed that the hybrid model produced better Value-at-Risk (VaR) accuracy under uncertainty and nonlinear conditions. Generally, models only use daily return data as model input. Based on research gaps, a hybrid model framework for estimating risk measures is proposed using a combination of EVT and ML, using multivariable and high-frequency data to identify extreme values in the distribution of data. The goal is to produce an accurate and flexible estimated risk value against extreme changes and shocks in the stock market. Mathematics Subject Classification: 60G25; 62M20; 6245; 62P05; 91G70.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"161-180"},"PeriodicalIF":2.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139486846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}