<![CDATA[ IEEE Transactions on Computational Social Systems - new TOC ]]>
http://ieeexplore.ieee.org
TOC Alert for Publication# 6570650 2022August 11<![CDATA[Table of Contents]]>94C1966196<![CDATA[IEEE Transactions on Computational Social Systems Publication Information]]>94C2C283<![CDATA[COVID-19’s Impact on Mental Health—The Hour of Computational Aid?]]>94967973485<![CDATA[Three-Party Evolutionary Game Model of Stakeholders in Mobile Crowdsourcing]]>949749852980<![CDATA[Robust Collaborative Filtering Recommendation With User-Item-Trust Records]]>$text {UIT}_{text {hybrid}}$ ), a novel approach that incorporates user trust into the existing CF-based methods in a harmonious way to supplement rating information. $text {UIT}_{text {hybrid}}$ employs multiple perspectives to extract proper services and achieves a good tradeoff between the robustness, accuracy, and diversity of the recommendation. We conduct extensive real-world experiments on the Epinions data set to demonstrate the feasibility and efficiency of $text {UIT}_{text {hybrid}}$ .]]>949869962766<![CDATA[SOIDP: Predicting Interlayer Links in Multiplex Networks]]>9499710072141<![CDATA[Compatible Influence Maximization in Online Social Networks]]>94100810191552<![CDATA[Research and Implementation of Chinese Couplet Generation System With Attention-Based Transformer Mechanism]]>94102010281570<![CDATA[Connections Between Relational Event Model and Inverse Reinforcement Learning for Characterizing Group Interaction Sequences]]>relational event model (REM) from the field of network science and inverse reinforcement learning (IRL) from the field of machine learning with respect to their ability to characterize sequences of directed social interaction events in group settings. REM is a conventional approach to tackle such a problem, whereas the application of IRL is a largely unbeaten path. We begin by examining the mathematical components of both REM and IRL and find straightforward analogies between the two methods and the unique characteristics of the IRL approach. We demonstrate the special utility of IRL in characterizing group social interactions with two empirical experiments. In the first experiment, we use IRL to infer personal behavioral preferences based on a sequence of directed communication events from a multiple-team system, specifically a group of virtual-reality game players interacting and cooperating to accomplish a shared goal. In the second experiment, we draw evidence from a machine-coded international event database called the Kansas Event Data System (KEDS) and use IRL to infer the national behavioral inclinations of four major regional and global powers (Iran, Iraq, Russia, and the USA) underlying their interactions with one another during the 1990s Gulf area struggles. Our comparison and experiments introduce fresh perspectives for social behavior analytics and help inspire new research opportunities at the nexus of social network analysis and machine learning.]]>94102910371069<![CDATA[Authorship Attribution of Microtext Using Capsule Networks]]>$n$ -grams for performing the AA task. Capsule with kervolutional neural networks (KNNs) has also been utilized for this task. We also present different analyses of our developed system, which improves the interpretability of our developed system. Heat-maps for different models illustrate the relevant text fragments for the prediction task. A standard Twitter data set is used for evaluating the performance of the developed systems. The experimental evaluation shows that capsule-based CNNs and capsule-based KNNs perform competitively and are able to outperform previous methods. The source codes and the supplementary file are available here https://github.com/chanchalIITP/AuthorIdentification.]]>94103810471922<![CDATA[Time-Series Snapshot Network for Partner Recommendation: A Case Study on OSS]]>94104810591943<![CDATA[A Kind of Change Management Method for Global Value Chain Optimization and Its Case Study]]>94106010742968<![CDATA[Skyline (<italic>λ</italic>, <italic>k</italic>)-Cliques Identification From Fuzzy Attributed Social Networks]]>$(lambda,k)$ -cliques over a fuzzy attributed social network and develop a formal concept analysis (FCA)-based skyline $(lambda,k)$ -cliques identification algorithm. Specifically, $lambda $ can be regarded as a quality control parameter for measuring the stability of the cohesive groups. Extensive experimental results conducted on three real-world datasets demonstrate the effectiveness of the skyline $(lambda,k)$ -clique model in a fuzzy attributed social network. Furthermore, an illustrative example is executed for revealing the usefulness of our model. It is expected that our proposed skyline $(lambda,k)$ -clique model can be widely used in various graph-based computational social systems, such as optimal team formation in crowdsourcing, and group recommendation in social networks.]]>94107510863671<![CDATA[Optimal Cyber-Insurance Contract Design for Dynamic Risk Management and Mitigation]]>94108711001162<![CDATA[Analysis of Public Sentiment on COVID-19 Vaccination Using Twitter]]>94110111113301<![CDATA[Cooperative and Parallel Fog Discovery and Pareto Optimal Fog Commerce Bargaining]]>fog resource discovery involves propagating resource requests to a huge number of fog nodes, fog commerce refers to the activity of buying and selling fog resources. This work devises: 1) the KM-gossip algorithm for bolstering fog discovery and 2) a bargaining mechanism for pricing fog resources. The KM-gossip algorithm is a generalization of the gossip algorithm. It uses $K$ broker agents (BAs) to cooperatively “gossip” requests among themselves, and in parallel, each BA relays the requests to $M$ fog nodes. While computational complexity analysis validates that the KM-gossip algorithm has logarithmic time complexity (i.e., it is computationally efficient), empirical results show that it significantly outperforms the existing gossip and flooding algorithms. Supplementing and complementing previous empirical results, game-theoretic analysis in this work validates that the bargaining mechanism generates Pareto optimal (economically efficient) solutions. The solutions also satisfy the famous Nash’s axioms that prescribe highly desirable properties of bargaining solutions.]]>94111211212254<![CDATA[Modeling the Impact of Social Distancing on the COVID-19 Pandemic in a Low Transmission Setting]]>94112211314699<![CDATA[Influence Spread in Location-Based Social Network: An Efficient Algorithm of Epidemic Controlling]]>MEI) problem aims to find a seed set with $k$ seed users such that the infection users can be minimized. In this article, we propose a piecewise function to measure the probability of each user being infected, which considers the distance and time. Then, we propose an algorithm called location-infected-greedy (LIG) to solve the MEI problem by finding the seed nodes that consider the probability of infection, time of check-in, location information, and influence of users. In the meantime, LIG can obtain an upper bound of the data-dependent approximate ratio, and it runs in $O(kn^{2})$ , where $n$ is the total number of nodes and $k$ is the number of seed nodes. Finally, extensive contrast experiments on real-world location-based social networks show that our algorithm is efficient and effective.]]>94113211434935<![CDATA[A Novel Approach to Select High-Reward Data Items in Big Data Stream Based on Multiarmed Bandit]]>$varepsilon $ -greedy, the improved upper confidence bound (UCB), and a data item selection policy named dynamic high-reward incentive (DHRI) with active, dynamic, and incentive reward. They are all trying to balance “exploitation and exploration” in a multiarmed bandit. Experimental results show that our proposed approach is effective and outperforms the traditional methods.]]>94114411531043<![CDATA[Real-Time Text Classification of User-Generated Content on Social Media: Systematic Review]]>94115411662510<![CDATA[A Robust Minimum-Cost Consensus Model With Uncertain Aggregation Weights Based on Data-Driven Method]]>94116711844620<![CDATA[Modeling and Simulating Adaptation Strategies Against Sea-Level Rise Using Multiagent Deep Reinforcement Learning]]>94118511961624<![CDATA[Multilabel Emotion Tagging for Domain-Specific Texts]]>94119712102891<![CDATA[Density-Peak-Based Overlapping Community Detection Algorithm]]>94121112232692<![CDATA[Computational Experiments for Complex Social Systems—Part II: The Evaluation of Computational Models]]>94122412367135<![CDATA[Exploiting Long- and Short-Term Preferences for Deep Context-Aware Recommendations]]>94123712483748<![CDATA[Vectorial-Opinion Dynamics With Familiarity Neighborhoods in Virtual Social Groups]]>94124912643162<![CDATA[SDH: Secure Data Hiding in Fused Medical Image for Smart Healthcare]]>94126512736123<![CDATA[IEEE Transactions on Computational Social Systems Society Information]]>94C3C3110<![CDATA[IEEE Transactions on Computational Social Systems Information for Authors]]>94C4C4120