Article
Computer Science, Interdisciplinary Applications
Peng Zhang, Xiao Zhang, Leyang Xue
Summary: In this paper, a heterogeneous spreading model using signed networks is proposed to study the impact of negative relationships on spreading. The study finds that the proportion and configuration of negative edges affect the balance of the network and the spreading coverage, with a random configuration exhibiting a suppressive effect on spreading.
INTERNATIONAL JOURNAL OF MODERN PHYSICS C
(2022)
Article
Mathematics, Interdisciplinary Applications
Ai-Wen Li, Xiao-Ke Xu, Ying Fan
Summary: In this study, strategies are proposed for controlling the spread of false information in signed social networks with positive and negative relationships. The results show that these strategies are more effective than existing methods.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Physics, Multidisciplinary
S. Arabzadeh, M. Sherafati, F. Atyabi, G. R. Jafari, K. Kulakowski
Summary: The paper investigates a fully connected network with signed links, considering the time evolution of the network towards a structurally balanced state. Results show that assigning a lifetime to each link leads to two asymptotic behaviors depending on the lifetime duration, with a crossover observed between them. The age distribution of links is found to depend on the lifetime, and the findings are discussed in the context of conflicts between political actors in Europe and the Middle East.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Junjie Huang, Ruobing Xie, Qi Cao, Huawei Shen, Shaoliang Zhang, Feng Xia, Xueqi Cheng
Summary: Most existing GNN-based recommender system models focus on learning personalized preferences from positive feedback. However, real-world recommender systems also involve negative feedback, which reflects users' personalized preferences. Utilizing negative feedback is a challenging research problem.
INFORMATION PROCESSING & MANAGEMENT
(2023)
Article
Engineering, Mechanical
Jiaxing Chen, Ying Liu, Jing Yue, Xi Duan, Ming Tang
Summary: The widespread dissemination of negative information on vaccines can hinder vaccination progress, but individual vaccination activities have a greater impact on epidemic spreading.
NONLINEAR DYNAMICS
(2022)
Article
Computer Science, Information Systems
Duoqi Song, Wenpei Wang, Ying Fan, Yanmeng Xing, An Zeng
Summary: In this study, a directed signed citation network was constructed using sentiment-labeled citation data to analyze papers in the field of Computational Linguistics. The research found that papers with different impacts have a similar probability of receiving negative citations, and highly cited papers tend to give negative citations to low-impact papers but avoid giving negative citations to high-impact papers.
INFORMATION PROCESSING & MANAGEMENT
(2022)
Article
Computer Science, Information Systems
Duoqi Song, Wenpei Wang, Ying Fan, Yanmeng Xing, An Zeng
Summary: The study reveals that negative citations have lower impact on highly cited papers, exhibit some randomness in distribution, and in the short term, negative citations are positively related to the impact of the cited paper, but negatively related in the long run.
INFORMATION PROCESSING & MANAGEMENT
(2022)
Article
Mathematics
Francisco Perez-Gamez, Domingo Lopez-Rodriguez, Pablo Cordero, Angel Mora, Manuel Ojeda-Aciego
Summary: Concepts and implications are two aspects of knowledge in a binary relation between objects and attributes. Simplification logic (SL) is valuable for studying attribute implications in concept lattices, and has become a core method for removing redundancy or obtaining different types of implication bases. This study proposes a mixed simplification logic and a method for automatically removing redundancy in implications, providing a foundational standpoint for automated reasoning methods in this extended framework.
Article
Biology
Monika Shpokayte, Olivia McKissick, Xiaonan Guan, Bingbing Yuan, Bahar Rahsepar, Fernando R. Fernandez, Evan Ruesch, Stephanie L. Grella, John A. White, X. Shawn Liu, Steve Ramirez
Summary: The hippocampus contains distinct populations of neurons responsible for processing positive and negative memories, which can be distinguished by their molecular, cellular, and projection-specific features. Recent studies have revealed cellular heterogeneity along the hippocampal axis, with the ventral hippocampus playing a significant role in emotion and valence processing. By combining different techniques, the researchers visualized valence-specific memory engrams in the ventral hippocampus, and found that these cells display different transcriptional programs and DNA methylation landscapes compared to neutral memory cells. Additionally, they demonstrated that stimulating the ventral hippocampus terminals projecting to certain brain regions can drive preference and avoidance behaviors. This study highlights the importance of the ventral hippocampus in processing appetitive and aversive memories.
COMMUNICATIONS BIOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Mingzhou Yang, Xingwei Wang, Lianbo Ma, Qiang He, Min Huang
Summary: This study proposes a novel structural balance model and designs an algorithm based on reinforcement learning to address the structural balance problem in signed social networks. Experimental results demonstrate that the algorithm outperforms other comparison algorithms in terms of optimal solutions, stability, and convergence.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Automation & Control Systems
Wenjun Mei, Ge Chen, Noah E. Friedkin, Florian Doerfler
Summary: This paper investigates how non-complete appraisal networks converge to structural balance while their graph topologies remain unchanged. Definitions of local balance and global balance are introduced, and two dynamics mechanisms are proposed to achieve local balance and global balance. Numerical studies provide insightful take-home messages.
Article
Computer Science, Hardware & Architecture
Rui Tang, Xingshu Chen, Chuancheng Wei, Qindong Li, Wenxian Wang, Haizhou Wang, Wei Wang
Summary: This paper proposes an interlayer link prediction framework based on multiple structural attributes (MulAtt) that calculates the matching degree of unmatched nodes once by leveraging the information of closed triad, intralayer links, matched neighbors, and intralayer links of neighbors simultaneously to ensure accuracy while reducing time consumption. The framework achieves better performance than several existing network structure-based methods in a non-iterative way.
Article
Computer Science, Artificial Intelligence
Yang Ou, Qiang Guo, Jia-Liang Xing, Jian-Guo Liu
Summary: This paper proposes a new graph convolutional network algorithm, M-RCNN, which identifies spreading influence nodes by considering structural information at micro, community, and macro levels. Experimental results demonstrate that M-RCNN achieves 9.25% higher accuracy compared to RCNN. The evaluation also shows that M-RCNN has a similar computational complexity to RCNN, indicating high efficiency.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Temirlan Kalimzhanov, Amir Haji Ali Khamseh'i, Aresh Dadlani, Muthukrishnan Senthil Kumar, Ahmad Khonsari
Summary: This paper explores the impact of positive and negative relationships on the spread of viral phenomena in social networks. Using an energy model based on Heider's balance theory, the study reveals the trade-off between social tension and epidemic spread. The analysis also highlights the role of hostile social links in the formation of disjoint friendly clusters.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Physics, Applied
Wenqiang Duan, Qinma Kang, Yunfan Kang, Jianwen Chen, Qingfeng Qin
Summary: This study presents a simple and effective iterated greedy algorithm for solving structural balance problems. The algorithm aims to minimize frustration and achieves high efficiency through a constructive greedy heuristic, a two-stage local search procedure, an adaptive destruction method, and two acceleration methods. Experimental results show that the proposed algorithm outperforms other meta-heuristics in terms of computational time.
INTERNATIONAL JOURNAL OF MODERN PHYSICS B
(2022)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
