IF 2.6 4区计算机科学

Big Data Pub Date : 2025-10-01 Epub Date: 2025-02-28 DOI: 10.1089/big.2024.0128

Ikpe Justice Akpan, Rouzbeh Razavi, Asuama A Akpan

{"title":"Evolutionary Trends in Decision Sciences Education Research from Simulation and Games to Big Data Analytics and Generative Artificial Intelligence.","authors":"Ikpe Justice Akpan, Rouzbeh Razavi, Asuama A Akpan","doi":"10.1089/big.2024.0128","DOIUrl":"10.1089/big.2024.0128","url":null,"abstract":"Decision sciences (DSC) involves studying complex dynamic systems and processes to aid informed choices subject to constraints in uncertain conditions. It integrates multidisciplinary methods and strategies to evaluate decision engineering processes, identifying alternatives and providing insights toward enhancing prudent decision-making. This study analyzes the evolutionary trends and innovation in DSC education and research trends over the past 25 years. Using metadata from bibliographic records and employing the science mapping method and text analytics, we map and evaluate the thematic, intellectual, and social structures of DSC research. The results identify \"knowledge management,\" \"decision support systems,\" \"data envelopment analysis,\" \"simulation,\" and \"artificial intelligence\" (AI) as some of the prominent critical skills and knowledge requirements for problem-solving in DSC before and during the period (2000-2024). However, these technologies are evolving significantly in the recent wave of digital transformation, with data analytics frameworks (including techniques such as big data analytics, machine learning, business intelligence, data mining, and information visualization) becoming crucial. DSC education and research continue to mirror the development in practice, with sustainable education through virtual/online learning becoming prominent. Innovative pedagogical approaches/strategies also include computer simulation and games (\"play and learn\" or \"role-playing\"). The current era witnesses AI adoption in different forms as conversational Chatbot agent and generative AI (GenAI), such as chat generative pretrained transformer in teaching, learning, and scholarly activities amidst challenges (academic integrity, plagiarism, intellectual property violations, and other ethical and legal issues). Future DSC education must innovatively integrate GenAI into DSC education and address the resulting challenges.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"416-437"},"PeriodicalIF":2.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527974","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}

引用次数: 0

The Impact of Cloaking Digital Footprints on User Privacy and Personalization. 隐藏数字足迹对用户隐私和个性化的影响。

IF 2.6 4区计算机科学

Big Data Pub Date : 2025-10-01 Epub Date: 2025-01-10 DOI: 10.1089/big.2024.0036

Sofie Goethals, Sandra Matz, Foster Provost, David Martens, Yanou Ramon

{"title":"The Impact of Cloaking Digital Footprints on User Privacy and Personalization.","authors":"Sofie Goethals, Sandra Matz, Foster Provost, David Martens, Yanou Ramon","doi":"10.1089/big.2024.0036","DOIUrl":"10.1089/big.2024.0036","url":null,"abstract":"Our online lives generate a wealth of behavioral records-digital footprints-which are stored and leveraged by technology platforms. These data can be used to create value for users by personalizing services. At the same time, however, it also poses a threat to people's privacy by offering a highly intimate window into their private traits (e.g., their personality, political ideology, sexual orientation). We explore the concept of cloaking: allowing users to hide parts of their digital footprints from predictive algorithms, to prevent unwanted inferences. This article addresses two open questions: (i) can cloaking be effective in the longer term, as users continue to generate new digital footprints? And (ii) what is the potential impact of cloaking on the accuracy of desirable inferences? We introduce a novel strategy focused on cloaking \"metafeatures\" and compare its efficacy against just cloaking the raw footprints. The main findings are (i) while cloaking effectiveness does indeed diminish over time, using metafeatures slows the degradation; (ii) there is a tradeoff between privacy and personalization: cloaking undesired inferences also can inhibit desirable inferences. Furthermore, the metafeature strategy-which yields more stable cloaking-also incurs a larger reduction in desirable inferences.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"345-363"},"PeriodicalIF":2.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142958560","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}

引用次数: 0

DMHANT: DropMessage Hypergraph Attention Network for Information Propagation Prediction. DMHANT：用于信息传播预测的 DropMessage 超图注意力网络。

IF 2.6 4区计算机科学

Big Data Pub Date : 2025-10-01 Epub Date: 2024-10-23 DOI: 10.1089/big.2023.0131

Qi Ouyang, Hongchang Chen, Shuxin Liu, Liming Pu, Dongdong Ge, Ke Fan

{"title":"DMHANT: DropMessage Hypergraph Attention Network for Information Propagation Prediction.","authors":"Qi Ouyang, Hongchang Chen, Shuxin Liu, Liming Pu, Dongdong Ge, Ke Fan","doi":"10.1089/big.2023.0131","DOIUrl":"10.1089/big.2023.0131","url":null,"abstract":"Predicting propagation cascades is crucial for understanding information propagation in social networks. Existing methods always focus on structure or order of infected users in a single cascade sequence, ignoring the global dependencies of cascades and users, which is insufficient to characterize their dynamic interaction preferences. Moreover, existing methods are poor at addressing the problem of model robustness. To address these issues, we propose a predication model named DropMessage Hypergraph Attention Networks, which constructs a hypergraph based on the cascade sequence. Specifically, to dynamically obtain user preferences, we divide the diffusion hypergraph into multiple subgraphs according to the time stamps, develop hypergraph attention networks to explicitly learn complete interactions, and adopt a gated fusion strategy to connect them for user cascade prediction. In addition, a new drop immediately method DropMessage is added to increase the robustness of the model. Experimental results on three real-world datasets indicate that proposed model significantly outperforms the most advanced information propagation prediction model in both MAP@k and Hits@K metrics, and the experiment also proves that the model achieves more significant prediction performance than the existing model under data perturbation.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"364-378"},"PeriodicalIF":2.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142512575","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}

引用次数: 0

Maximizing Influence in Social Networks Using Combined Local Features and Deep Learning-Based Node Embedding. 利用组合本地特征和基于深度学习的节点嵌入，最大化社交网络中的影响力。

IF 2.6 4区计算机科学

Big Data Pub Date : 2025-10-01 Epub Date: 2024-10-22 DOI: 10.1089/big.2023.0117

Asgarali Bouyer, Hamid Ahmadi Beni, Amin Golzari Oskouei, Alireza Rouhi, Bahman Arasteh, Xiaoyang Liu

{"title":"Maximizing Influence in Social Networks Using Combined Local Features and Deep Learning-Based Node Embedding.","authors":"Asgarali Bouyer, Hamid Ahmadi Beni, Amin Golzari Oskouei, Alireza Rouhi, Bahman Arasteh, Xiaoyang Liu","doi":"10.1089/big.2023.0117","DOIUrl":"10.1089/big.2023.0117","url":null,"abstract":"The influence maximization problem has several issues, including low infection rates and high time complexity. Many proposed methods are not suitable for large-scale networks due to their time complexity or free parameter usage. To address these challenges, this article proposes a local heuristic called Embedding Technique for Influence Maximization (ETIM) that uses shell decomposition, graph embedding, and reduction, as well as combined local structural features. The algorithm selects candidate nodes based on their connections among network shells and topological features, reducing the search space and computational overhead. It uses a deep learning-based node embedding technique to create a multidimensional vector of candidate nodes and calculates the dependency on spreading for each node based on local topological features. Finally, influential nodes are identified using the results of the previous phases and newly defined local features. The proposed algorithm is evaluated using the independent cascade model, showing its competitiveness and ability to achieve the best performance in terms of solution quality. Compared with the collective influence global algorithm, ETIM is significantly faster and improves the infection rate by an average of 12%.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"379-397"},"PeriodicalIF":2.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142480288","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}

引用次数: 0

Optimizing Multilayer Networks Through Time-Dependent Decision-Making: A Comparative Study. 基于时间依赖决策的多层网络优化：比较研究。

IF 2.6 4区计算机科学

Big Data Pub Date : 2025-10-01 Epub Date: 2025-07-08 DOI: 10.1089/big.2024.0094

Kenan Menguc, Alper Yilmaz

{"title":"Optimizing Multilayer Networks Through Time-Dependent Decision-Making: A Comparative Study.","authors":"Kenan Menguc, Alper Yilmaz","doi":"10.1089/big.2024.0094","DOIUrl":"10.1089/big.2024.0094","url":null,"abstract":"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.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"398-415"},"PeriodicalIF":2.6,"publicationDate":"2025-10-01","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}

引用次数: 0

A Study of Public Opinion Reversal Recognition of Emergency Based on Hypernetwork. 基于超网络的突发事件舆情逆转识别研究。

IF 2.6 4区计算机科学

Big Data Pub Date : 2025-08-22 DOI: 10.1177/2167647X251366060

Xuna Wang

{"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":"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.","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}

引用次数: 0

A Fast Survival Support Vector Regression Approach to Large Scale Credit Scoring via Safe Screening. 通过安全筛选进行大规模信用评分的快速生存支持向量回归方法。

IF 2.6 4区计算机科学

Big Data Pub Date : 2025-08-01 Epub Date: 2024-07-23 DOI: 10.1089/big.2023.0033

Hong Wang, Ling Hong

引用次数: 0

Dual-Path Graph Neural Network with Adaptive Auxiliary Module for Link Prediction. 带自适应辅助模块的双路径图神经网络用于链路预测

IF 2.6 4区计算机科学

Big Data Pub Date : 2025-08-01 Epub Date: 2024-03-25 DOI: 10.1089/big.2023.0130

Zhenzhen Yang, Zelong Lin, Yongpeng Yang, Jiaqi Li

{"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":"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.","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}

引用次数: 0

Content-Aware Human Mobility Pattern Extraction. 内容感知的人类移动模式提取。

IF 2.6 4区计算机科学

Big Data Pub Date : 2025-08-01 Epub Date: 2024-07-10 DOI: 10.1089/big.2022.0281

Shengwen Li, Chaofan Fan, Tianci Li, Renyao Chen, Qingyuan Liu, Junfang Gong

{"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":"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.","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}

引用次数: 0

Research on the Influence of Information Iterative Propagation on Complex Network Structure. 信息迭代传播对复杂网络结构的影响研究。

IF 2.6 4区计算机科学

Big Data Pub Date : 2025-08-01 Epub Date: 2024-07-27 DOI: 10.1089/big.2023.0016

Yinuo Qian, Fuzhong Nian, Zheming Wang, Yabing Yao

{"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":"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.","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}

引用次数: 0

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