Article
Computer Science, Information Systems
Ling Chen, Yuliang Zhang, Yixin Chen, Bin Li, Wei Liu
Summary: This paper discusses the issue of negative influence blocking maximization in social networks, introduces the IBM-US problem, and showcases several algorithms for addressing this problem. Experimental results demonstrate that the proposed algorithms can yield higher quality results than other methods.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Chao Liu, Haichao Xu, Xiaoyang Liu
Summary: In this paper, we study the problem of online influence maximization in social networks and address the challenges of continuous independent cascade model and node-edge-level feedback. We propose a continuous independent cascade model by introducing a continuous influence set, which allows for multiple activations and customization to attract targeted users. We also improve the IMFB algorithm by using node-edge-level feedback to generate source nodes, and propose the CIC_IMFB-NE algorithm, which outperforms existing algorithms in terms of regret bound in real life.
KNOWLEDGE AND INFORMATION SYSTEMS
(2023)
Article
Physics, Multidisciplinary
Yudong Gong, Sanyang Liu, Yiguang Bai
Summary: Influence maximization is a critical problem, but faces challenges with the increase in network scale and diverse network topologies. The PDSA algorithm, with its probability-driven and structure-aware approach, demonstrates superior performance and robustness in solving IM problems.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Dong Li, Yuejiao Wang, Muhao Li, Xin Sun, Jingchang Pan, Jun Ma
Summary: This research introduces the problem of maximizing net positive influence in signed social networks and proposes an improved greedy algorithm to solve it, with experiments demonstrating the effectiveness of this approach.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Physics, Multidisciplinary
Pei Li, Ke Liu, Keqin Li, Jianxun Liu, Dong Zhou
Summary: This study introduces a duplicate forwarding model to characterize the diffusion process in social networks and theoretically analyze the user influence in the independent cascade model. After obtaining user influence ranking, a Spearman-like correlation coefficient is proposed to measure the correlation between rankings, showing that the analysis results from the duplicate forwarding model achieve better accuracy in estimating user influence ranking compared to traditional methods.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Xiaoyan Yin, Xiao Hu, Yanjiao Chen, Xu Yuan, Baochun Li
Summary: This paper investigates influence maximization for advertisement recommendation in signed social networks, proposing a new framework and algorithm that effectively influence a broader range of individuals.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Information Systems
Sohameh Mohammadi, Mohammad H. Nadimi-Shahraki, Zahra Beheshti, Kamran Zamanifar
Summary: In this study, a fuzzy-based approach is introduced to model influence propagation for different user-relationship types and four novel fuzzy sign-aware diffusion models are proposed. The experimental results show that the proposed models enhance prediction accuracy and make effective decisions in viral marketing.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Dong Li, Jiming Liu
Summary: Research on influence propagation models over signed social networks revealed that existing models are stochastic and descriptive, requiring a significant number of Monte-Carlo simulations which are time-consuming and not scalable. The proposed PLID model can quickly and accurately calculate user sets' influence without simulations; extensive experiments showed that the PLID model outperforms state-of-the-art methods in positive influence spread and running time.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Review
Physics, Multidisciplinary
Bo-Lun Chen, Wen-Xin Jiang, Yi-Xin Chen, Ling Chen, Rui-Jie Wang, Shuai Han, Jian-Hong Lin, Yi-Cheng Zhang
Summary: This review provides a comprehensive survey and analysis of the theory and applications of influence blocking maximization, filling the gap in methodological and theoretical advances in influence blocking maximization problem from the perspective of social network influence analysis.
PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Liqing Qiu, Yuying Liu, Xiuliang Duan
Summary: This study proposes an improved heuristic algorithm BHICM for the influence maximization problem. The algorithm selects seed nodes based on a dynamic relationship strategy and diffusion score, striking a balance between accuracy and efficiency.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
WeiMin Li, Zheng Li, Alex Munyole Luvembe, Chao Yang
Summary: The paper proposes an influence maximization algorithm based on the Gaussian propagation model, which improves effectiveness and efficiency through multidimensional space modeling and parameter control.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Yadan Luo, Zi Huang, Hongxu Chen, Yang Yang, Hongzhi Yin, Mahsa Baktashmotlagh
Summary: Signed link prediction in social networks aims to reveal the underlying relationships (i.e., links) among users (i.e., nodes) given their existing interactions. Existing graph-based approaches lack human-intelligible explanations for key questions, and thus a new framework, SIHG, is proposed. SIHG incorporates a signed attention module to identify representative neighboring nodes and preserve the geometry of antagonism. Extensive experiments demonstrate that SIHG outperforms existing methods in signed link prediction.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
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
Mathematics, Interdisciplinary Applications
Wei Lin, Qikui Xu, Yifan Li, Li Xu
Summary: Studies on multiplex temporal networks (MTNs) can lead to a more precise understanding of real-world systems. In this work, the relationship between the controllability of data transmission and edge centrality in MTNs is explored, considering factors affecting spatio-temporal modeling of data transmission and how they relate to network topology. Simulation experiments confirm that topological features considering temporal and multiplex factors can improve edge ranking accuracy and identify the most influential edges.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Computer Science, Information Systems
Qiang He, Lihong Sun, Xingwei Wang, Zhenkun Wang, Min Huang, Bo Yi, Yuantian Wang, Lianbo Ma
Summary: The study explores positive opinion maximization by utilizing the "Activated Opinion Maximization Framework" (AOMF) in signed social networks, which includes three phases: selection of candidate seed nodes, activated opinion formation process, and determination of seed nodes. Experimental results demonstrate that the proposed method outperforms chosen benchmarks in terms of potential opinions and positive ratio.
INFORMATION SCIENCES
(2021)
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)
