IEEE Transactions on Computational Social Systems最新文献

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Advertiser-First: A Receding Horizon Bid Optimization Strategy for Online Advertising 广告主优先:网络广告出价优化策略
IF 4.5 2区 计算机科学
IEEE Transactions on Computational Social Systems Pub Date : 2024-10-31 DOI: 10.1109/TCSS.2024.3476694
Ke Fang;Hao Liu;Chao Li;Junfeng Wu;Yang Tan;Qiuqiang Lin;Qingyu Cao
{"title":"Advertiser-First: A Receding Horizon Bid Optimization Strategy for Online Advertising","authors":"Ke Fang;Hao Liu;Chao Li;Junfeng Wu;Yang Tan;Qiuqiang Lin;Qingyu Cao","doi":"10.1109/TCSS.2024.3476694","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3476694","url":null,"abstract":"Online advertising has been the mainstream monetization approach for internet-based companies, in which bid optimization plays a crucial role in enhancing advertising performance. Currently, the bid optimization problem has narrowed down to two specific forms: Budget-constrained bidding (BCB) and Multi-constraint bidding (MCB). Existing solutions try to solve BCB/MCB via linear programming solvers, learning methods, or feedback control. However, in large-scale complex e-commerce, they still suffer from inefficiency, poor convergence, or slow adaptation to the changing market. This research presents an online receding optimization method as a solution for practical bid optimization problems. We conduct a theoretical analysis of the optimal bidding strategy's structure. Further, an online receding optimization process is designed based on open-loop feedback control, which periodically updates a constructed optimal bid formulation that can be solved by linear programming. Then, considering large-scale linear programming problems, we propose an efficient down sampling scheme. Besides, a neural-network-based auction scale prediction is used to adapt to the changing market. Finally, a series of online A/B experiments on <italic>Taobao Sponsored Search</i> compare our work to industrial methods and state-of-the-art from several aspects. The proposed method has been implemented on <italic>Taobao</i>, a billion-scaled online advertising business, for over a year.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1132-1144"},"PeriodicalIF":4.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Deep Similarity Graph Fusion for Multiview Clustering 多视图聚类的深度相似图融合
IF 4.5 2区 计算机科学
IEEE Transactions on Computational Social Systems Pub Date : 2024-10-30 DOI: 10.1109/TCSS.2024.3479188
Weijun Sun;Zhikun Jiang;Yonghao Chen;Jiaqing Li;Chengbin Zhou;Na Han
{"title":"Deep Similarity Graph Fusion for Multiview Clustering","authors":"Weijun Sun;Zhikun Jiang;Yonghao Chen;Jiaqing Li;Chengbin Zhou;Na Han","doi":"10.1109/TCSS.2024.3479188","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3479188","url":null,"abstract":"The graph-based multiview clustering has gained significant attention due to its effectiveness in representing complex relationships among multiview data for enhanced clustering. Among the previous graph-based methods, the multiview graph learning (or graph fusion) technique has rapidly emerged as a promising direction, which, however, still suffers from two critical limitations. First, most of previous methods adopt a single-level of graph fusion, which lack the ability to go from single-level graph fusion to multilevel (deep) graph fusion. Second, they generally focus on constructing an optimal unified graph but cannot fully investigate the correlations among multiple views. Therefore, it is difficult to establish a comprehensive and obvious graph structure. In light of this, this article presents a new multiview graph learning method called deep similarity graph fusion (DSGF) for the multiview clustering task, where three pathways are simultaneously leveraged to fuse multilevel similarity into a unified graph. Particularly, multilevel graph fusion is utilized to obtain a view-specific similarity graph for each view and then fuse these single-view graphs (via three levels of graph fusion) into a robust graph, which takes advantage of deeper consensus information between various similarity graphs and improves the quality of the learned graph for the final spectral clustering process. Extensive experiments are conducted on six real-world multiview datasets, which demonstrate the highly competitive clustering performance of DSGF in comparison with state-of-the-art methods.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"435-446"},"PeriodicalIF":4.5,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Consistency and Controversy Analysis in the Hype of Room-Temperature Superconductivity 室温超导炒作中的一致性与争议分析
IF 4.5 2区 计算机科学
IEEE Transactions on Computational Social Systems Pub Date : 2024-10-30 DOI: 10.1109/TCSS.2024.3479727
Tao Chen;Baoyu Zhang;Weishan Zhang;Tao Wang;Xiao Wang;Qiang Li;Fei-Yue Wang
{"title":"Consistency and Controversy Analysis in the Hype of Room-Temperature Superconductivity","authors":"Tao Chen;Baoyu Zhang;Weishan Zhang;Tao Wang;Xiao Wang;Qiang Li;Fei-Yue Wang","doi":"10.1109/TCSS.2024.3479727","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3479727","url":null,"abstract":"Room-temperature superconductors (esp. LK-99 in the recent) have attracted extensive academic attention in recent years, both in academic circles and among the general public. This topic has spread through a number of social media channels, a plethora of contradiction in information has emerged within social networks. There arises the question on how to analyze the consistency and controversy of such scientific knowledge in the dissemination process, and how this process impact on public cognition on the scientific knowledge. In this article, taking room-temperature superconductor as example, we first designed a large language model based factual consistency detection approach to analyze the consistency between research papers and media reports. Then the consistency between media reports and comments is analyzed, by proposing a novel quantification method for media agenda-setting capability, which evaluates the agenda-setting capability of media based on emotional and positional consistencies. The results indicate that two significant deviations occur when room-temperature superconductor knowledge is spread from specialized fields to the public through the various media. One deviation is due to the specialized nature of room-temperature superconductor knowledge, leading to discrepancies between reported content and factual information in research papers. The other deviation is caused by conflicting knowledge, resulting in disparities between media reports and public perception.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1013-1024"},"PeriodicalIF":4.5,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 Blockchain-Empowered Multiaggregator Federated Learning Architecture in Edge Computing With Deep Reinforcement Learning Optimization 基于深度强化学习优化的边缘计算中基于区块链的多聚合器联邦学习架构
IF 4.5 2区 计算机科学
IEEE Transactions on Computational Social Systems Pub Date : 2024-10-30 DOI: 10.1109/TCSS.2024.3481882
Xiao Li;Weili Wu
{"title":"A Blockchain-Empowered Multiaggregator Federated Learning Architecture in Edge Computing With Deep Reinforcement Learning Optimization","authors":"Xiao Li;Weili Wu","doi":"10.1109/TCSS.2024.3481882","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3481882","url":null,"abstract":"Federated learning (FL) is emerging as a sought-after distributed machine learning architecture, offering the advantage of model training without direct exposure to raw data. With advancements in network infrastructure, FL has been seamlessly integrated into edge computing. However, the limited resources on edge devices introduce security vulnerabilities to FL in the context. While blockchain technology promises to bolster security, practical deployment on resource-constrained edge devices remains a challenge. Moreover, the exploration of FL with multiple aggregators in edge computing is still new in the literature. Addressing these gaps, we introduce the blockchain-empowered heterogeneous multiaggregator federated learning architecture (BMA-FL). We design a novel lightweight Byzantine consensus mechanism, namely PBCM, to enable secure and fast model aggregation and synchronization in BMA-FL. We study the heterogeneity problem in BMA-FL that the aggregators are associated with varied number of connected trainers with non-IID data distributions and diverse training speed. We propose a multiagent deep reinforcement learning algorithm (MASB-DRL) to help aggregators decide the best training strategies. Experiments on real-word datasets demonstrate the efficiency of BMA-FL to achieve better models faster than baselines, showing the efficacy of PBCM and MASB-DRL.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"645-657"},"PeriodicalIF":4.5,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Pricing of Product Line Along With Its Value-Added Services With Consideration of Effects of Reference Price 考虑参考价格影响的产品线及其增值服务定价
IF 4.5 2区 计算机科学
IEEE Transactions on Computational Social Systems Pub Date : 2024-10-29 DOI: 10.1109/TCSS.2024.3479271
Wei Qi;Nan Li;Junwei Wang;Xinggang Luo
{"title":"Pricing of Product Line Along With Its Value-Added Services With Consideration of Effects of Reference Price","authors":"Wei Qi;Nan Li;Junwei Wang;Xinggang Luo","doi":"10.1109/TCSS.2024.3479271","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3479271","url":null,"abstract":"Customers’ price comparison behavior and search cost evaluation play a pivotal role in shaping purchasing utility and enterprise pricing strategies. This study employs the multinomial logit (MNL) model to integrate the reference price and search cost into product line design. We probe into pure bundle and mixed bundle pricing decisions for the product line and value-added services in monopolistic settings and delve into the impacts of the reference price and search cost on pricing, profit, product variety, and strategy. Based on model analysis and numerical simulations, the results show that the reference price effect benefits low-priced offerings but undermines high-priced products, overall market share, and profit. As the reference price effect or search cost increases, product variety diminishes. In scenarios where the reference price effect and the proportion of bundled purchases are minimal, the pure bundling strategy is preferable; otherwise, the mixed bundling strategy is more advantageous. When enterprises overlook the reference price effect, there is an imbalance in the pricing of various products and services within the product line, which results in the actual market share and profit consistently falling below the ideal expectations.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"447-462"},"PeriodicalIF":4.5,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Fine-Grained Emotion Comprehension: Semisupervised Multimodal Emotion and Intensity Recognition 细粒度情绪理解:半监督多模态情绪和强度识别
IF 4.5 2区 计算机科学
IEEE Transactions on Computational Social Systems Pub Date : 2024-10-29 DOI: 10.1109/TCSS.2024.3475511
Zheng Fang;Zhen Liu;Tingting Liu;Chih-Chieh Hung
{"title":"Fine-Grained Emotion Comprehension: Semisupervised Multimodal Emotion and Intensity Recognition","authors":"Zheng Fang;Zhen Liu;Tingting Liu;Chih-Chieh Hung","doi":"10.1109/TCSS.2024.3475511","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3475511","url":null,"abstract":"The rapid advancement of deep learning and the exponential growth of multimodal data have led to increased attention on multimodal emotion analysis and comprehension in affect computing. While existing multimodal works have achieved notable results in emotion recognition, several challenges remain. First, the scarcity of public large-scale multimodal emotion datasets is attributed to the high cost of manual annotation and the subjectivity of handcrafted labels. Second, most approaches only focus on learning emotion category information, disregarding the crucial evaluation indicator of emotion intensity, which hampers the development of fine-grained emotion recognition. Third, a significant emotion semantic discrepancy exists in different modalities, and current methodologies struggle to bridge the cross-modal gap and effectively utilize a vast amount of unlabeled emotion data, hindering the production of high-quality pseudolabels and superior classification performance. To address these challenges, based on the multitask learning architecture, we propose a novel semisupervised fine-grained emotion recognition model SMEIR-net for multimodal emotion and intensity recognition. Concretely, in semisupervised learning (SSL) phase, we design multistage self-training and consistency regularization paradigm to generate high-quality pseudolabels. Then, in supervised learning phase, we leverage multimodal transformer fusion and adversarial learning to eliminate the cross-modal semantic discrepancy. Extensive experiments are conducted on three benchmark datasets, namely RAVDESS, eNTERFACE, and Lombard-GRID, to evaluate the proposed model. The series sets of experimental results demonstrate that our SSL model successfully utilizes multimodal data and available labels to transfer emotion and intensity information from labeled to unlabeled datasets. Moreover, the corresponding evaluation metrics demonstrate that the utilize high-quality pseudolabels can achieve superior emotion and intensity classification performance, which outperforms other state-of-the-art baselines under the same condition.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1145-1163"},"PeriodicalIF":4.5,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Forwarding in Social Media: Forecasting Popularity of Public Opinion With Deep Learning 社交媒体中的转发:利用深度学习预测舆论流行度
IF 4.5 2区 计算机科学
IEEE Transactions on Computational Social Systems Pub Date : 2024-10-29 DOI: 10.1109/TCSS.2024.3468721
Yongqing Yang;Chenghao Fan;Yeming Gong;William Yeoh;Yuan Li
{"title":"Forwarding in Social Media: Forecasting Popularity of Public Opinion With Deep Learning","authors":"Yongqing Yang;Chenghao Fan;Yeming Gong;William Yeoh;Yuan Li","doi":"10.1109/TCSS.2024.3468721","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3468721","url":null,"abstract":"The forwarding behavior of social media users within social circles facilitates intensive discussions of specific social events in cyberspace, significantly contributing to the dissemination and development of public opinions. Existing models for calculating the popularity of public opinion (PPO) overlook the effects of forwarding behavior. This article addresses this gap with two primary objectives: 1) by developing a calculation model for PPO that integrates the forwarding dynamics within social networks; and 2) by establishing a predictive model that is applied to the temporal evolution of forwarding circles, thus enabling a time-series prediction for PPO. The approach commenced by determining the information entropy based on the structural attributes of forwarding circles. Then, we assess the similarity between information entropy production and the Baidu search index to validate the calculation model’s accuracy. Building on this foundation, public opinion data centered around 30 social events with a total sample size of 15.567 million blogs were collected for modeling. Finally, we design a deep learning algorithm to predict the PPO trend. The results demonstrate that the information entropy of forwarding circles accurately represents PPO, and the proposed predictive model can capture the time-series evolution trend of PPO on social media. These findings offer valuable insights into public opinion analysis and present a robust method for academics and social media practitioners.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"749-763"},"PeriodicalIF":4.5,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737887","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Generating-Based Attacks to Online Social Networks 基于生成的在线社交网络攻击
IF 4.5 2区 计算机科学
IEEE Transactions on Computational Social Systems Pub Date : 2024-10-24 DOI: 10.1109/TCSS.2024.3461710
Tianchong Gao;Yucheng Bian;Feng Li;Agnideven Palanisamy Sundar
{"title":"Generating-Based Attacks to Online Social Networks","authors":"Tianchong Gao;Yucheng Bian;Feng Li;Agnideven Palanisamy Sundar","doi":"10.1109/TCSS.2024.3461710","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3461710","url":null,"abstract":"Online social network (OSN) privacy leakage problem addresses more and more users’ concerns. Studying the problem from attackers’ view could tell us how to prevent further data leakage. Currently, attackers mainly focus on mapping identities between their background knowledge and the published data to collect useful information. However, it becomes difficult to find the global optimal mapping strategy because of the complexity of the OSN data. This article proposes a novel generating-based attack on OSN data, no longer restricted to mapping-based information collection. Generally, the proposed scheme learns OSN properties from the attackers’ background knowledge and employs the knowledge to fill the unknown area in the published data. The proposed scheme employs a generative adversarial network to ensure the similarity between the generated graph and the published data. The conditional information is also added in the generation process such that the generated graph is restricted to the conditions under attackers’ background knowledge. Experimental results show that the proposed scheme successfully infer private information with real-world OSN datasets.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7881-7891"},"PeriodicalIF":4.5,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
LLM Plus Machine Learning Outperform Expert Rating to Predict Life Satisfaction From Self-Statement Text LLM +机器学习优于专家评级,从自我陈述文本预测生活满意度
IF 4.5 2区 计算机科学
IEEE Transactions on Computational Social Systems Pub Date : 2024-10-22 DOI: 10.1109/TCSS.2024.3475413
Feng Huang;Xia Sun;Aizhu Mei;Yilin Wang;Huimin Ding;Tingshao Zhu
{"title":"LLM Plus Machine Learning Outperform Expert Rating to Predict Life Satisfaction From Self-Statement Text","authors":"Feng Huang;Xia Sun;Aizhu Mei;Yilin Wang;Huimin Ding;Tingshao Zhu","doi":"10.1109/TCSS.2024.3475413","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3475413","url":null,"abstract":"This study explores an innovative approach to predicting individual life satisfaction by combining large language models (LLMs) with machine learning (ML) techniques. Traditional life satisfaction assessments rely on self-report questionnaires, which can be time-consuming and resource intensive. To address these limitations, we developed a method that utilizes LLMs for feature extraction from open-ended self-statement texts, followed by ML prediction. We compared this approach with standalone LLM predictions and expert ratings. A sample of 378 participants completed the satisfaction with life scale (SWLS) and wrote self-statements about their current life situation. The LLM-based ML model, using a LightGBM regressor, achieved a correlation of 0.542 with self-reported SWLS scores, outperforming both the standalone LLM (<italic>r</i> <inline-formula><tex-math>$=$</tex-math></inline-formula> 0.491) and expert ratings (<italic>r</i> <inline-formula><tex-math>$=$</tex-math></inline-formula> 0.455). Effect size analysis revealed a statistically significant moderate effect size difference between the LLM-based ML model and expert ratings (Cohen's <italic>d</i> <inline-formula><tex-math>$=$</tex-math></inline-formula> 0.499, 95% CI [0.043, 0.955]). These findings demonstrate the potential of integrating LLM and ML for an efficient and accurate assessment of life satisfaction, challenging conventional methods, and opening new avenues for psychological measurement. The study's implications extend to research, clinical practice, and policymaking, offering promising advancements in AI-assisted psychological assessment.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1092-1099"},"PeriodicalIF":4.5,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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
Adapting GNNs for Document Understanding: A Flexible Framework With Multiview Global Graphs 适应gnn用于文档理解:一个具有多视图全局图的灵活框架
IF 4.5 2区 计算机科学
IEEE Transactions on Computational Social Systems Pub Date : 2024-10-21 DOI: 10.1109/TCSS.2024.3468890
Zhuojia Wu;Qi Zhang;Duoqian Miao;Xuerong Zhao;Kaize Shi
{"title":"Adapting GNNs for Document Understanding: A Flexible Framework With Multiview Global Graphs","authors":"Zhuojia Wu;Qi Zhang;Duoqian Miao;Xuerong Zhao;Kaize Shi","doi":"10.1109/TCSS.2024.3468890","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3468890","url":null,"abstract":"Graph neural networks (GNNs) have recently gained attention for capturing complex relations, prompting researchers to explore their potential in document classification. Existing studies serving this purpose fall into two directions: inductive learning focusing on personalized context relations within documents and transductive learning targeting the global distribution relations among documents in a corpus. Both directions extract distinct types of beneficial structural information and yield encouraging outcomes. However, due to the incompatibility of underlying graph structures and learning settings, developing an enhanced model that effectively integrates local and global relational learning within existing frameworks is challenging. To address this issue, we propose a new GNN-based document representation learning framework that incorporates multiview global graphs at both the word and document levels, focusing on learning the diverse global distribution information of texts at different granularities. Additionally, a contextual encoder derives the initial representations of document nodes from the updated representations of word nodes, integrating personalized context relations into document representations during this process. Finally, we tailor a node representation learning strategy for the multiview global graphs, called the multiview graph sampling and updating module, which allows our framework to operate efficiently during training without being constrained by the scale of the global graph. Experiments indicate that our framework generally enhances performance by integrating both global and local relational learning. When combined with large-scale language models, our framework achieves state-of-the-art results for GNN-based models across multiple datasets.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"608-621"},"PeriodicalIF":4.5,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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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