Article
Computer Science, Artificial Intelligence
Wenjian Luo, Daofu Zhang, Li Ni, Nannan Lu
Summary: In this paper, a new method for multiscale local community detection is proposed, which can discover local communities of different scales by introducing a new local modularity. Experimental results on multiple datasets indicate that the detected communities are meaningful and their scale can be reasonably adjusted.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Cybernetics
Jinyin Chen, Yixian Chen, Lihong Chen, Minghao Zhao, Qi Xuan
Summary: This article discusses the issue of community detection attacks and proposes the evolutionary perturbation attack (EPA) method, which successfully attacks network community algorithms at different scales, showing better performance than baseline attack methods.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Amit Paul, Animesh Dutta
Summary: Detecting communities in complex networks is a challenging task due to their unknown properties. In this study, a Local Group Assimilation (LGA) algorithm is proposed to identify clusters or communities in a network graph using both local and global structure information. The algorithm achieves promising results in detecting significant communities in real networks and compares favorably to other state-of-the-art algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Review
Computer Science, Information Systems
Norah Alotaibi, Delel Rhouma
Summary: This article provides an overview of the characteristics and challenges of community detection in dynamic social networks, and compares state-of-the-art methods. Researchers can use this survey to find the best methods and choose relevant future directions.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Mohammad Ebrahim Samie, Eileen Behbood, Ali Hamzeh
Summary: Social network analysis has opened up new research areas and researchers are constantly improving existing methods to meet analysts' needs. Applying the concept of Two-phase Influence Maximization can improve community detection methods, and a new technique has been proposed to solve the problem of time-consuming calculations in dynamic networks. Experimental results showed significant improvements in both static and dynamic social networks.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Hardware & Architecture
Shiva Jalali, Monireh Hosseini
Summary: Recommender systems are crucial in providing high-quality suggestions and modeling users' dynamic behaviors; social networks offer new data types for personalization and performance enhancement; a method combining dynamic collaborative filtering and social recommendation techniques can address sparsity and cold start challenges effectively.
JOURNAL OF SUPERCOMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Hongtao Liu, Jiahao Wei, Tianyi Xu
Summary: In this paper, a new community detection method called CPGC is proposed, which combines the community perspective and graph convolution network to address the challenges of overlapping communities in attributed networks. CPGC achieves state-of-the-art results in nonoverlapping or overlapping communities, as demonstrated by experiments on various real-world networks.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Cybernetics
Boyu Li, Dany Kamuhanda, Kun He
Summary: This study introduces a novel approach called centroid-based multiple local community detection (C-MLC) to automatically determine the number of communities containing a query node and uncover the corresponding communities using high-quality seeds.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2022)
Article
Computer Science, Information Systems
Shunjie Yuan, Hefeng Zeng, Ziyang Zuo, Chao Wang
Summary: This study proposes an overlapping community detection method CDMG based on Graph Convolutional Networks (GCN) to maximize the Markov stability of community structure. Extensive experiments demonstrate the superiority of CDMG compared to other established community detection algorithms. The performance of CDMG can be further improved by utilizing the optimal Markov time, which is found using a trichotomy-based method based on the influence of Markov time on CDMG performance.
COMPUTER COMMUNICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Marco De Luca, Anna Rita Fasolino, Antonino Ferraro, Vincenzo Moscato, Giancarlo Sperli, Porfirio Tramontana
Summary: In this paper, a novel heterogeneous graph-based model is proposed to capture and handle the complex and strongly-correlated information of a software Developer Social Network (DSN) for analytic tasks. The problem of automatically discovering communities of software developers sharing interests for similar projects is addressed using Social Network Analysis (SNA) findings, and graph embedding techniques are utilized to overcome the large graph size. The proposed approach is evaluated against state-of-the-art approaches in terms of efficiency and effectiveness using the GitHub dataset.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Zhe Chen, Aixin Sun, Xiaokui Xiao
Summary: The article introduces an incremental community detection framework inc-AGGMMR, which maps attributes into the network by constructing an augmented graph and balances the contribution between attribute and topological information through weight adjustment.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2021)
Article
Computer Science, Information Systems
Georgia Baltsou, Konstantinos Christopoulos, Konstantinos Tsichlas
Summary: Local community detection is an important and promising research field with applications in various domains. This paper provides a comprehensive overview and taxonomy of local community detection algorithms, filling the research gap and helping researchers gain a clear understanding of the problem.
Article
Automation & Control Systems
Fengnan Gao, Zongming Ma, Hongsong Yuan
Summary: This paper presents a simple community detection algorithm that achieves consistency and optimality for a broad and flexible class of sparse latent space models. The algorithm is based on spectral clustering and local refinement through normalized edge counting, and it is easy to implement and computationally efficient. The proof of optimality is based on an interesting equivalence between likelihood ratio test and edge counting in a simple vs. simple hypothesis testing problem, which could be of independent interest.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Kun Guo, Xintong Huang, Ling Wu, Yuzhong Chen
Summary: This paper proposes a local community detection algorithm based on local modularity density, which incorporates new considerations in both the core area detection and community extension stages to improve robustness to seed node selection. Experimental results demonstrate that the algorithm can accurately and stably detect local communities.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Yingkui Wang, Di Jin, Dongxiao He, Katarzyna Musial, Jianwu Dang
Summary: The study of community detection in networks has gained significant attention, particularly in understanding the formation of community structure based on specific user social behaviors. By exploring reciprocity of interactions, posting preference, multitopic preference, and temporal variation of topics, researchers have proposed a generative community detection model, SBCD, that combines network topology and content and outperforms existing baselines in detecting communities with topics.
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)