IEEE Transactions on Computational Social Systems最新文献_第9页

Learning Multiagent Cooperation via Reciprocity-Based Actor-Critic Framework 基于互惠的行动者-批评框架的多智能体合作学习

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-06-26 DOI: 10.1109/TCSS.2026.3705509

Jia Long;Dengxiu Yu;C. L. Philip Chen

{"title":"Learning Multiagent Cooperation via Reciprocity-Based Actor-Critic Framework","authors":"Jia Long;Dengxiu Yu;C. L. Philip Chen","doi":"10.1109/TCSS.2026.3705509","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3705509","url":null,"abstract":"In multiagent mixed-motive games, the dynamic adaptation among agent policies induces a nonstationary environment, posing challenges to stable learning and convergence toward Pareto-optimal equilibrium. To address this, this article proposes a reciprocity-based actor-critic (AC) framework by embedding a mutual help mechanism, which encourages agents to balance self-interest with the preferences of others. Our approach utilizes a centralized critic to estimate Q-values and infer expected policies of other agents, while decentralized actors concurrently update individual policies through selective altruistic adjustments. By integrating expected policy computation and redefining the advantage function, it promotes agent cooperation without sacrificing individual interests, thus mitigating local optimality. A policy improvement lower bound is established to ensure monotonic convergence with theoretical guarantees for Pareto improvement. Furthermore, we design a hybrid neural architecture that combines a shared backbone network for global knowledge transfer with personalized branches for individual policy optimization. This structure balances coordination and adaptation by synthesizing extrinsic environmental feedback with intrinsic agent motivation. Experimental results validate the effectiveness of our method in enhancing behavior coordination and learning efficiency in decentralized multiagent settings. Internal feedback, External feedback","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"5595-5607"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097564","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

Semantic Augmented Multiview Graph Learning With Frequency-Aware Propagation for Recommendation 基于频率感知传播的语义增强多视图学习推荐

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-08-14 DOI: 10.1109/TCSS.2026.3716309

Yixin Liu;Yu Zhang;Lei Sang;Yiwen Zhang

{"title":"Semantic Augmented Multiview Graph Learning With Frequency-Aware Propagation for Recommendation","authors":"Yixin Liu;Yu Zhang;Lei Sang;Yiwen Zhang","doi":"10.1109/TCSS.2026.3716309","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3716309","url":null,"abstract":"Graph neural networks (GNNs) have been widely adopted in collaborative filtering to model higher order relationships between users and items. However, existing methods mainly focus on interaction-based structures and neglect the explicit modeling of semantic relations among users and among items. Within these graph structures, deep graph message propagation tends to cause over-smoothing, where node embeddings converge to similar representations, hindering effective personalization. To address these challenges, we propose a semantic-augmented multiview graph-based recommendation framework (SAGRec). Specifically, SAGRec constructs semantic similarity graphs based on embeddings generated by large language models to capture semantic relations between users and between items. Next, we propose a frequency-aware propagation mechanism. It leverages representation differences between nodes and incorporates gated message passing to retain node distinctions during propagation. To further mitigate representation drift induced by multilayer propagation, we design an adaptive inter-layer contrastive learning strategy that enforces consistency while maintaining discriminability across layers. Comprehensive empirical evaluations conducted on three real-world datasets show that SAGRec achieves consistently superior performance over baseline methods with respect to normalized discounted cumulative gain and recall.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"5907-5918"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097584","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 New Information Theoretical Measure for Node Similarity in Complex Networks 复杂网络中节点相似度的一种新的信息理论度量

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2025-12-17 DOI: 10.1109/TCSS.2025.3630179

Ricky X. F. Chen;Meng-Ting Wang

{"title":"A New Information Theoretical Measure for Node Similarity in Complex Networks","authors":"Ricky X. F. Chen;Meng-Ting Wang","doi":"10.1109/TCSS.2025.3630179","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3630179","url":null,"abstract":"Measuring the similarity of nodes is a topic of significance in network science. The effectiveness of a method depends on how well it captures both the local and global features. In this article, we propose a new method for measuring node similarity based on <inline-formula><tex-math>$[t,p]$</tex-math></inline-formula>-spectra of nodes induced by <inline-formula><tex-math>$[t,p]$</tex-math></inline-formula>-separate chain decompositions of graphs, a recently introduced framework both preserving the interaction information of local neighborhoods and implicitly capturing the higher-order associative features of nodes by iteratively filtering subgraph chains. Specifically, the structural profile of a node is represented by its adjacency <inline-formula><tex-math>$[t,p]$</tex-math></inline-formula>-spectrum and the Jensen–Shannon divergence is used to quantify the similarity of the profiles of two nodes. The difference of the spreading powers under the susceptible-infected (SI) model of node <inline-formula><tex-math>$v$</tex-math></inline-formula> and the most similar node of <inline-formula><tex-math>$v$</tex-math></inline-formula> identified in this way is then used as a validation of our method. Extensive statistical analyses on disparate real-world networks from social, biological, and transportation domains show that the proposed method achieves higher accuracy in quantifying node similarity in most networks than five benchmark methods.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6491-6501"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097687","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 Macro-to-Micro Layout Optimization Scheme for Functional Interwoven Areas of Commercial Complex 商业综合体功能交织区宏微观布局优化方案

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-01-01 DOI: 10.1109/TCSS.2025.3644359

Jingxuan Peng;Zhonghua Wei;Xueying Tong;Xuwen Jia;Yanyan Chen;Yongxing Li;Fujiyama Taku

{"title":"A Macro-to-Micro Layout Optimization Scheme for Functional Interwoven Areas of Commercial Complex","authors":"Jingxuan Peng;Zhonghua Wei;Xueying Tong;Xuwen Jia;Yanyan Chen;Yongxing Li;Fujiyama Taku","doi":"10.1109/TCSS.2025.3644359","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3644359","url":null,"abstract":"In commercial complexes, the functional interwoven areas (FIA) with multiple attributes shops accommodate diverse population of pedestrians with heterogeneous travel purposes. A well-designed layout scheme of FIA can effectively increase the revenue while reduce the pedestrian congestion. However, existing layout schemes are more developed from the operator’s perspective, focusing on maximizing rents while neglecting shop attributes and pedestrians heterogeneity, which results in excessive corridor congestion and suboptimal store visit rates. To address these issues, this article proposes a novel macro-to-micro layout optimization framework that enhances pedestrian in-store rate while mitigating internal congestion. The proposed framework consists of two stages: 1) a macrolevel optimization model which generates multiple feasible layout schemes by using shop attributes based attractiveness algorithm; and 2) a microlevel simulation model which evaluates pedestrian movement dynamics and corridor density under different layouts by integrating an attractive potential based social force model (AP-SFM). Based on parameters calibrated from real collected data, we conduct extensive numerical and simulation experiments across two distinct commercial complexes: 1) China World Mall (CWM); and 2) Shin Kong Place (SKP). The results demonstrate that the optimized layouts significantly increase pedestrian in-store rates while effectively reducing average corridor density and alleviating local congestion, without altering the total pedestrian volume. Furthermore, the successful application in two distinct commercial complexes proves the validity, rationality, and generality of the proposed framework. Our research effectively integrates shop diversity and pedestrian heterogeneity into shop layout optimization, providing a practical and scalable decision-support tool for layout design and management.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6371-6389"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097827","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

Structure Dynamics and Evolution Mechanisms of Technology Knowledge Transfer Networks 技术知识转移网络的结构动力学与演化机制

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-03-06 DOI: 10.1109/TCSS.2026.3673913

Baoxiang Li

{"title":"Structure Dynamics and Evolution Mechanisms of Technology Knowledge Transfer Networks","authors":"Baoxiang Li","doi":"10.1109/TCSS.2026.3673913","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3673913","url":null,"abstract":"Building an extensive technology knowledge transfer network (TKFNS) is critical for entities in emerging technology integration industries (e.g., biomedical engineering) to absorb external key knowledge, mitigate R&D risks, and improve knowledge innovation capability. However, existing studies mostly focus on single relational TKFNS and static network analysis, failing to reveal the structural dynamic characteristics and evolution mechanisms of TKFNS embedded with both explicit and implicit knowledge transfer. We apply the identified complex relationship data of innovation cooperation and right transfer from 2011 to 2020 to construct TKFNS and use social network analysis (SNA) and temporal exponential random graph model (TERGM) to explore its structural dynamic characteristics and evolution mechanisms. It is found that there are structure dependent effects in TKFNS, and convergence, multiconnectivity, and transfer closure are obvious. There is time dependent effect in TKFNS, and path dependence exists. Organization homogeneity, geography homogeneity, and geography proximity have significant contributions to network evolution, but the innovation capability, cognitive proximity, and social proximity do not. This study promotes our understanding of TKFNS and is of great revelation to accelerate the knowledge transfer of emerging technology industries.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6161-6173"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097991","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

Variational Inference of Parameters in Opinion Dynamics Models 意见动态模型中参数的变分推理

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-01-05 DOI: 10.1109/TCSS.2025.3640692

Jacopo Lenti;Fabrizio Silvestri;Gianmarco De Francisci Morales

{"title":"Variational Inference of Parameters in Opinion Dynamics Models","authors":"Jacopo Lenti;Fabrizio Silvestri;Gianmarco De Francisci Morales","doi":"10.1109/TCSS.2025.3640692","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3640692","url":null,"abstract":"Understanding human behavior represents a paramount challenge in modern social systems. This task must be tackled with tools that both explain the mechanisms underlying the social dynamics and efficiently handle vast amounts of data. While agent-based models (ABMs) are generally used as simulating tools to describe social dynamics, their connection to data is lackluster. Instead, here we adopt a probabilistic machine learning approach for fitting ABMs to real data. To this end, we propose a variational inference (VI) framework that estimates the macroscopic and microscopic parameters of several opinion dynamics models. Our methodology encompasses three steps: (i) translation of the opinion dynamics models into probabilistic generative models (PGMs), (ii) relaxation of discrete variables to make the models differentiable, and (iii) estimation of the parameters and latent variables via stochastic VI (SVI). Experiments show that VI improves over existing methods in estimating discrete and continuous variables, both at the microscopic and macroscopic scales, in all four different categories of rules opinion dynamics models. Moreover, VI effectively estimates high-dimensional variables, up to 400 agent-level attributes, and is faster than the alternatives.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6670-6681"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097007","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

Improved SAC Algorithm for Solving the Interactive Hybrid Disassembly Line Balancing Problem Considering Multiskilled Workers 考虑多技术工人的交互式混合装配线平衡问题的改进SAC算法

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-04-20 DOI: 10.1109/TCSS.2026.3675008

QingBo Meng;XiWang Guo;ZhiWei Zhang;JiaCun Wang;ShuJin Qin;GuiPeng Xi;Liang Qi

{"title":"Improved SAC Algorithm for Solving the Interactive Hybrid Disassembly Line Balancing Problem Considering Multiskilled Workers","authors":"QingBo Meng;XiWang Guo;ZhiWei Zhang;JiaCun Wang;ShuJin Qin;GuiPeng Xi;Liang Qi","doi":"10.1109/TCSS.2026.3675008","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3675008","url":null,"abstract":"With the advancement of manufacturing and the progression towards intelligent production, more and more production enterprises are focusing on the hybrid disassembly line balancing problem to enhance efficiency and reduce costs. This work presents an interactive hybrid disassembly line balancing problem considering multiskilled workers and its corresponding mathematical model. Additionally, we incorporated the concept of carbon emissions into the problem to ensure the green sustainability of the production process. We employ an improved soft actor-critic (SAC) algorithm for solving the problem to achieve the goals of maximizing profit, minimizing idle time, and minimizing carbon emissions. The experimental results demonstrate that the improved SAC algorithm performs well on six different experimental scale cases. Specifically, multiobjective soft actor-critic (MO-SAC) produces solutions that are closer to the Pareto front than those obtained by deep deterministic policy gradient and nondominated sorting genetic algorithm II. This advantage is particularly pronounced in medium and large-scale problem instances. Statistical comparisons using nondominated solution metrics (spread, epsilon, and IGD+), Pareto front distributions, and computational time show that MO-SAC achieves better convergence and maintains comparable computational efficiency, highlighting its performance benefits in complex scenarios. The results verify the effectiveness of our proposed approach, highlighting the superiority of the improved SAC algorithm in addressing the multiobjective interactive hybrid disassembly line balancing problem.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6117-6131"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097072","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

Instance-Aware Kernel Prediction for Efficient Point Cloud Learning 高效点云学习的实例感知核预测

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-07-21 DOI: 10.1109/TCSS.2026.3713377

Yuxiao Feng;Shengjie Zhao;Jin Zeng

{"title":"Instance-Aware Kernel Prediction for Efficient Point Cloud Learning","authors":"Yuxiao Feng;Shengjie Zhao;Jin Zeng","doi":"10.1109/TCSS.2026.3713377","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3713377","url":null,"abstract":"As a pivotal modality for 3-D perception, point clouds are essential for various applications ranging from autonomous driving to immersive 3-D reconstruction. However, robust and efficient point cloud representation learning is often hindered by the limitations of static, instance-agnostic neighborhood construction. Existing architectures predominantly rely on Euclidean proximity for local feature aggregation, which inevitably leads to feature contamination at semantic boundaries and prohibitive computational overhead when modeling fine-grained geometric details. To address these challenges, we propose the instance-aware kernel prediction network (IKPNet), a novel framework that integrates semantic consistency into hierarchical representation learning. Specifically, we develop a lightweight score head to generate a semantic affinity matrix that is progressively refined across network layers. This matrix enables dynamic, instance-aware neighborhood construction, ensuring that feature aggregation is governed by both geometric proximity and semantic coherence. By sharing and iteratively updating this affinity matrix, IKPNet facilitates the capture of nonlocal semantic correlations while significantly reducing redundant multi-layer perceptron (MLP) computations. Extensive experiments on multiple benchmarks demonstrate that IKPNet achieves state-of-the-art performance, including 94.1% classification accuracy on ModelNet40 and 74.9% mean intersection over union (mIoU) on S3DIS Area 5, while maintaining computational efficiency. Our results indicate that bridging the gap between low-level geometry and high-level semantic consistency is crucial for precise and scalable 3-D scene understanding.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6023-6034"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097238","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

Concurrent Acoustic Feature Learning for Robust Speech Emotion Recognition Under Data Scarcity 基于并行声学特征学习的数据稀缺鲁棒语音情感识别

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-06-25 DOI: 10.1109/TCSS.2026.3703492

Samuel Kakuba;Dong Seog Han

{"title":"Concurrent Acoustic Feature Learning for Robust Speech Emotion Recognition Under Data Scarcity","authors":"Samuel Kakuba;Dong Seog Han","doi":"10.1109/TCSS.2026.3703492","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3703492","url":null,"abstract":"Speech emotion recognition (SER) continues to struggle with limited labeled data and ineffective integration of heterogeneous acoustic features. Without an effective integration mechanism, emotional cues which would otherwise be complementary to each other may be lost, reducing model performance. Existing deep learning approaches for unimodal audio-based SER still suffer from suboptimal understanding of inter and intra feature relationships between complementary features and distinguishing emotions in the same arousal plane. To address this, we propose the concurrent acoustic feature learning (CAFLE) model. The CAFLE model employs intermediate fusion of heterogeneous, emotionally rich features extracted from segment-level speech using the temporal salience gate, lightweight convolution transformers blocks which are fused in cross-level interaction attention module (CLIM) that performs cross-stream attention to generate discriminative MFCCs and melspectrogram fusion features. This model is further reinforced with convolutional neural network encoders, transformer encoders and self-attentive BiLSTM layers. Evaluation results indicate that the CAFLE model consistently outperforms conventional methods. The CAFLE model achieves 93.65%, 98.81%, 95.03%, 94.58%, 84.00%, and 95.16% of accuracy and 94.04%, 98.45%, 95.46%, 95.22%, 84.24%, and 95.27% of F1 score when evaluated on Ryerson audio-visual database of emotional speech and song (RAVDESS), Berlin emotional speech database (EMODB), SAVEE, KESDy18, CREMA, and the ASVP unseen datasets, respectively. From the comparative evaluation with benchmark approaches, the CAFLE model’s performance results indicate an improvement of more than 6.08% in recognition accuracy and 6.72% in F1 score compared to baseline models. The highest improvement of 18.8% in detection accuracy and 19.57% of F1 score is recorded when the CAFLE model is evaluated on the Korean language KESDy18 datasets.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"5719-5732"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11578433","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097179","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

ULP-Swap: A Unified Liquidity Pool for Financial Reuse of Gaming NFTs via Pool Rentals and ABP-Secured Swaps ULP-Swap：通过池租赁和abp担保掉期为游戏nft的金融再利用提供统一的流动性池

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-07-14 DOI: 10.1109/TCSS.2026.3708983

Haoran Yin;Jiaxiang Sun;Rong Zhao;Chenyu Zhou;Yuhua Jin

{"title":"ULP-Swap: A Unified Liquidity Pool for Financial Reuse of Gaming NFTs via Pool Rentals and ABP-Secured Swaps","authors":"Haoran Yin;Jiaxiang Sun;Rong Zhao;Chenyu Zhou;Yuhua Jin","doi":"10.1109/TCSS.2026.3708983","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3708983","url":null,"abstract":"Blockchain games provide verifiable ownership of in-game assets through nonfungible tokens (NFTs), but they still face high entry barriers, low capital efficiency, and fragmented asset liquidity across isolated game ecosystems. Existing NFT finance mechanisms remain largely separated: rental systems often require dedicated collateral or centralized coordination, while peer-to-peer swaps of unequal-value assets typically rely on additional balance margins. To address these limitations, we design a unified NFT liquidity and Swapping System centered on a unified liquidity pool (ULP). In our design, users deposit eligible NFTs into the ULP, and a time-weighted average price (TWAP)-based valuation module maps their deposited assets into a unified credit layer quantified by total borrowing power (TBP) and available borrowing power (ABP). This shared risk layer supports two application markets: 1) a pool rental market, where users access NFTs through their existing ABP; and 2) a peer-to-peer swap market, where the value difference in an unequal-value swap is secured by the lower-value party’s <inline-formula><tex-math>$mathbf{ABP}_{mathbf{net}}$</tex-math></inline-formula> rather than an additional cash margin. We further present the protocol architecture, core accounting logic, and a prototype-level evaluation based on smart-contract gas analysis and multiagent simulation. Under the modeled assumptions, the results indicate improved rental accessibility, higher capital utilization, and reduced additional capital-locking requirements for NFT swaps, supporting the feasibility of more flexible financial reuse of gaming NFTs through a unified credit layer.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"5875-5890"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097525","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