IEEE Transactions on Computational Social Systems最新文献_第7页

Diffusion of Ordinal Opinions in Social Networks: An Agent-Based Model and Heuristics for Campaigning 社会网络中有序意见的扩散：一个基于主体的竞选模型与启发式

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-09 DOI: 10.1109/TCSS.2024.3458950

Xiaoxue Liu;Shohei Kato;Wen Gu;Fenghui Ren;Guoxin Su;Minjie Zhang

{"title":"Diffusion of Ordinal Opinions in Social Networks: An Agent-Based Model and Heuristics for Campaigning","authors":"Xiaoxue Liu;Shohei Kato;Wen Gu;Fenghui Ren;Guoxin Su;Minjie Zhang","doi":"10.1109/TCSS.2024.3458950","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3458950","url":null,"abstract":"Most research investigating how social influence affects election results mainly uses diffusion models for binary opinions. However, these diffusion models are progressive and focus on the diffusion of one opinion. In this article, we introduce a general diffusion model for ordinal opinions expressed as linear orderings over a finite set of candidates. We employ agent-based modeling to simulate a nonprogressive diffusion process, allowing multiple types of opinion diffusion about different candidates. The proposed agent-based diffusion model can forecast long-term trends of opinion diffusion in social networks by capturing voters’ personalized features and incorporating dynamic social contexts. Furthermore, we examine the possibility of affecting election outcomes by externally changing the ordinal opinions of certain vertices, i.e., campaigning. Since finding influential voters from the social network is computationally challenging, we propose a heuristic approach, i.e., backward influence rank (BIR). Experimental results demonstrate that the proposed BIR approach is superior to the classic greedy approach for campaigning by achieving a similar margin of victory to that of the greedy approach but running two orders of magnitude faster than the greedy approach did.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"335-347"},"PeriodicalIF":4.5,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106588","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

Enhanced Group Influence Maximization in Social Networks Using Deep Reinforcement Learning 利用深度强化学习增强社交网络中的群体影响力最大化

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-04 DOI: 10.1109/TCSS.2024.3459853

Smita Ghosh;Tiantian Chen;Weili Wu

{"title":"Enhanced Group Influence Maximization in Social Networks Using Deep Reinforcement Learning","authors":"Smita Ghosh;Tiantian Chen;Weili Wu","doi":"10.1109/TCSS.2024.3459853","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3459853","url":null,"abstract":"In contemporary society, groups are pivotal in shaping decisions and actions. The consensus of a majority of members on specific topics often guides the collective decision-making in groups. Group influence maximization (GIM) aims to select <inline-formula><tex-math>$k$</tex-math></inline-formula> seed users in a network to maximize the number of eventually activated groups. A group is said to be activated if <inline-formula><tex-math>$beta$</tex-math></inline-formula> percent of users in this group are activated. This study delves into the strategic selection of seed users in social networks to maximize the spread of a topic, thereby activating the highest number of groups. The GIM problem, inherently NP-hard when computing the influence spread from a selected set of nodes, has traditionally faced obstacles in ensuring theoretical robustness, time efficiency, and adaptability in large and complex network environments. To overcome these challenges, we introduce a robust framework called GIMDRL that addresses the GIM problem in social networks using deep reinforcement learning (DRL). Our approach integrates node embeddings from multiple graph neural networks, thereby utilizing diverse information for effective network analysis. This integration plays a crucial role in optimizing the parameter learning process. Extensive experiments are conducted on real-world and synthetic datasets to assess the performance of our proposed framework. The results of these experiments indicate that our approach significantly outperforms existing methods in GIM, even when trained on sampled graphs. This highlights our model's strong capacity for generalization in varying network scenarios.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"573-585"},"PeriodicalIF":4.5,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783309","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

SpaTeD: Sparsity-Aware Tensor Decomposition-Based Representation Learning Framework for Phishing Scams Detection 基于稀疏感知张量分解的网络钓鱼诈骗检测表示学习框架

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-04 DOI: 10.1109/TCSS.2024.3462552

Medhasree Ghosh;Raju Halder;Joydeep Chandra

{"title":"SpaTeD: Sparsity-Aware Tensor Decomposition-Based Representation Learning Framework for Phishing Scams Detection","authors":"Medhasree Ghosh;Raju Halder;Joydeep Chandra","doi":"10.1109/TCSS.2024.3462552","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3462552","url":null,"abstract":"In recent years, the consequences of phishing scams on Ethereum have adversely affected the stability of the cryptocurrency environment. Numerous incidents have been reported that have resulted in a substantial loss of cryptocurrency. The existing literature in this area primarily leverages traditional feature engineering or network representation learning to recover crucial information from transaction records to identify suspected users. However, these methods mainly rely on handcrafted feature engineering or conventional node representation learning from a static network while ignoring the network dynamism and inherent temporal sparsity in the user behavior that results in underperformance after an extended period. This article proposes a novel sparsity-aware tensor decomposition-based architecture: <italic>SpaTeD</i>, which retrieves efficient user representation utilizing the evolving transaction and structural information and subsequently mitigates the temporal sparsity problem. Our model is evaluated on a real-world Ethereum phishing scam dataset and reports a significant performance improvement over the baselines (96% <italic>recall</i> and 96% <italic>F1-score</i>). We have conducted an extensive set of experiments to verify the temporal robustness of the model. Additionally, we have provided the ablation study to demonstrate the contribution of each component of the framework.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"320-334"},"PeriodicalIF":4.5,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106587","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

Deformable Blur Sensing and Regression Analysis ReID Feature Fusion for Multitarget Multicamera Tracking Systems in Highway Scenarios 公路场景下多目标多摄像机跟踪系统的变形模糊感知与回归分析

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-04 DOI: 10.1109/TCSS.2024.3454321

Sixian Chan;Shenghao Ni;Bin Guo;Jie Hu;Tinglong Tang;Xiaolong Zhou;Pengyi Hao

{"title":"Deformable Blur Sensing and Regression Analysis ReID Feature Fusion for Multitarget Multicamera Tracking Systems in Highway Scenarios","authors":"Sixian Chan;Shenghao Ni;Bin Guo;Jie Hu;Tinglong Tang;Xiaolong Zhou;Pengyi Hao","doi":"10.1109/TCSS.2024.3454321","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3454321","url":null,"abstract":"In highway scenarios, the rapid motion of vehicles can cause deformation and blur in camera footage, significantly affecting the accuracy of vehicle detection and re-identification (ReID) in multitarget multicamera tracking (MTMCT) systems. To address this issue, this article develops the deformable and blur sensing and regression analysis ReID feature fusion MTMCT system (DSRF). First, a deformable and blur sensing detection module (DFB) in DSRF is designed to overcome the limitations of cameras in capturing fast-moving objects, thereby accurately detecting vehicles moving at high speeds on highways. Then, a regression-based ReID feature fusion algorithm (RARF) in DSRF is proposed, which enhances ReID features by modeling the relationship between vehicle motion and its features, thereby better associating the detected vehicles in consecutive frames into trajectories and establishing intertrajectory relationships. Finally, extensive experiments are conducted on the highway surveillance traffic (HST) dataset developed by our team and the public dataset (CityFlow). Promising results are achieved, validating the effectiveness of our proposed method.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"738-748"},"PeriodicalIF":4.5,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783349","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 Subspace-Based Method for Facial Image Editing 基于子空间的人脸图像编辑方法

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-04 DOI: 10.1109/TCSS.2024.3447692

Nan Yang;MengChu Zhou;Xin Luan;Liang Qi;Yandong Tang;Zhi Han

{"title":"A Subspace-Based Method for Facial Image Editing","authors":"Nan Yang;MengChu Zhou;Xin Luan;Liang Qi;Yandong Tang;Zhi Han","doi":"10.1109/TCSS.2024.3447692","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3447692","url":null,"abstract":"In the realm of computational social systems, the ability to edit facial attributes accurately plays a crucial role in enhancing user experience on social media platforms and virtual environments. However, we face significant challenges in isolated attribute manipulation and balancing the tradeoff between editing fidelity and facial identity preservation. Here, this article presents a novel approach to constructing an orthogonal decomposition subspace, enabling precise editing control over individual attributes with minimal impact on others and maintaining identity consistency. We introduce an adaptive weight modulation (AWM) method and a maximum slope truncation (MST) formula. The AWM method, founded on a sufficient convergent criterion, performs singular value decomposition to yield subspace parameters that preserve rich facial knowledge within the generative model, facilitating high-quality facial generation with reduced parameterization. This empowers meaningful semantic interpretation of attributes, supporting diverse editing tasks such as pose, age, and eyewear adjustments. The MST formula rigorously defines the editing bounds to effectively navigate the tradeoff between editing depth and identity retention. We also propose a guideline for deciphering the specific meanings of unsupervised semantics, potentially advancing interpretability in social behavioral studies. An accompanying web application, available at <uri>https://github.com/mickoluan/GreenLimeSia</uri>, has been developed, granting users the freedom to perform tailored facial edits. Extensive experimental results show we pave the way for more personalized and authentic interactions within computational social platforms.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"185-197"},"PeriodicalIF":4.5,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361448","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

IEEE Transactions on Computational Social Systems Information for Authors 电气和电子工程师学会计算社会系统论文集作者信息

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-02 DOI: 10.1109/TCSS.2024.3457715

引用次数: 0

Computational Aids on Mental Health: Revolutionizing Care in the Digital Age 心理健康计算辅助工具：数字时代的护理革命

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-02 DOI: 10.1109/TCSS.2024.3458128

Shiqiu Meng;Jie Shi;Bin Hu

引用次数: 0

IEEE Systems, Man, and Cybernetics Society Information 电气和电子工程师学会系统、人和控制论学会信息

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-02 DOI: 10.1109/TCSS.2024.3457713

引用次数: 0

IEEE Transactions on Computational Social Systems Publication Information 电气和电子工程师学会《计算社会系统期刊》出版信息

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-02 DOI: 10.1109/TCSS.2024.3457711

引用次数: 0

Video Echoed in Harmony: Learning and Sampling Video-Integrated Chord Progression Sequences for Controllable Video Background Music Generation 视频回声在和谐：学习和采样视频集成和弦进行序列可控视频背景音乐的产生

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-01 DOI: 10.1109/TCSS.2024.3451515

Xinyi Tong;Sitong Chen;Peiyang Yu;Nian Liu;Hui Qv;Tao Ma;Bo Zheng;Feng Yu;Song-Chun Zhu

{"title":"Video Echoed in Harmony: Learning and Sampling Video-Integrated Chord Progression Sequences for Controllable Video Background Music Generation","authors":"Xinyi Tong;Sitong Chen;Peiyang Yu;Nian Liu;Hui Qv;Tao Ma;Bo Zheng;Feng Yu;Song-Chun Zhu","doi":"10.1109/TCSS.2024.3451515","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3451515","url":null,"abstract":"Automatically generating video background music mitigates the inefficiency and time-consuming drawbacks of current manual video editing. Two key challenges hinder the expansion of the inception of video-to-music tasks. 1) Limited availability of high-quality video–music datasets and annotations. 2) Absence of music generation methods that consider actual musicality, which are controlled by interpretable factors based on music theory. In the article, we propose video echoed in harmony (VEH), a method for learning and sampling video-integrated chord progression sequences. Our approach adopts harmony, represented by chord progressions that are aligned with various music formats [musical instrument digital interface (MIDI), audio, and score], imitating chord precedence in human music composition. Visual-language models link visual features to chord progressions through genre labels and descriptive words in generated textualized videos. The two aforementioned features collectively obviate the necessity of extensive video–music paired data. Besides, an energy-based chord progression learning and sampling algorithm quantifies abstract harmony impressions to statistical features, serving as interpretable factors for the controllable music generation based on music theory. Experimental results demonstrate that the proposed method outperforms the state-of-the-art, producing a superior music alignment for the given video.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"905-917"},"PeriodicalIF":4.5,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777859","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