Article
Computer Science, Artificial Intelligence
Aman Ullah, Bin Wang, JinFang Sheng, Jun Long, Nasrullah Khan, ZeJun Sun
Summary: Recognition of vital nodes in complex networks is crucial for improving network robustness and vulnerability. Existing methods have limitations, leading to the proposal of a Local-and-Global Centrality (LGC) algorithm to address both local and global aspects of network topology simultaneously. Experiments show that LGC outperforms many state-of-the-art techniques in identifying vital nodes.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Haotian Zhang, Shen Zhong, Yong Deng, Kang Hao Cheong
Summary: In this article, a novel centrality measure based on local fuzzy information centrality (LFIC) is proposed and its effectiveness is verified through multiple experiments. The results indicate that this method can identify influential nodes that cause wider scope of infection and larger effect on network connectivity. Furthermore, an extension method is proposed for weighted directed complex networks.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Physics, Multidisciplinary
Yong Yu, Biao Zhou, Linjie Chen, Tao Gao, Jinzhuo Liu
Summary: In recent years, the identification of essential nodes in complex networks has gained significant attention. This paper proposes a novel importance metric called node propagation entropy, which accurately and stably identifies significant nodes.
Article
Mathematics
Yanjie Xu, Tao Ren, Shixiang Sun
Summary: This study proposes an agglomerative community detection algorithm based on node influence and the similarity of nodes. Experimental results show that the algorithm is effective in community detection and it is quite comparable to existing classic methods.
Article
Computer Science, Artificial Intelligence
Renny Marquez, Richard Weber
Summary: Community detection is a crucial task in social network analysis, but static networks may not capture the dynamics of real-world problems. We propose CoDeDANet, an algorithm that detects communities in dynamic attributed networks by considering both link and node information. By optimizing the importance of attributes based on spectral clustering and incorporating tensors to capture past information, CoDeDANet outperforms other state-of-the-art community detection algorithms in tests on synthetic and real-world networks.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Bin Wang, Wanghao Guan, Yuxuan Sheng, Jinfang Sheng, Jinying Dai, Junkai Zhang, Qiong Li, Qiangqiang Dong, Long Chen
Summary: This study introduces a new method for identifying influential nodes in complex networks, taking into account the attraction between nodes, effectively addressing the issue of node influence. Extensive experiments have shown that this method outperforms representative algorithms in terms of accuracy and time complexity.
INTERNATIONAL JOURNAL OF MODERN PHYSICS C
(2021)
Article
Computer Science, Cybernetics
Wenzhi Liu, Pengli Lu, Teng Zhang
Summary: This article proposes a centrality model called semi-local and global centrality (SLGC) based on the semi-local and global structure of nodes, which can evaluate the importance of nodes more comprehensively. The semi-local influence (SLI) is reflected through the definition of generalized energy and the construction of first-order and second-order generalized energy entropies. The global influence (GI) is constructed based on the clustering coefficients of nodes and the distance between nodes. The effectiveness and versatility of SLGC are evaluated through comparison with benchmark methods on nine real networks, and the results show good performance in monotonicity, resolution, accuracy, and top-10 nodes.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Aman Ullah, Bin Wang, JinFang Sheng, Jun Long, Nasrullah Khan, Muhammad Ejaz
Summary: Identification of patronized communities in complex networks is crucial for network analysis. Existing methods are unable to effectively address the severity of the problem. We propose a novel Relevance-based Information Interaction Model (RIIM) that can identify communities in complex networks without prior knowledge and configuration. Extensive experiments demonstrate the superior performance of RIIM in identifying communities in complex networks.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Hardware & Architecture
Fahimeh Dabaghi-Zarandi, Parsa KamaliPour
Summary: In this paper, a novel community detection method is proposed that utilizes both local and global network information, resulting in low complexity and high accuracy. The method divides the network graph into different community structures by identifying and storing similarity measures and assigning appropriate weights to nodes and links based on local network information. The best community structure is selected using evaluation functions based on both local and global network information. Experimental results demonstrate that the proposed method achieves good detection performance and evaluation functions in various networks.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2022)
Article
Mathematics
Attila Mester, Andrei Pop, Bogdan-Eduard-Madalin Mursa, Horea Grebla, Laura Diosan, Camelia Chira
Summary: Evaluation of important nodes in a network can be done through different centrality measures and community detection algorithms, providing overlapping results and complementary information on important nodes.
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)
Article
Computer Science, Information Systems
Shen Zhong, Haotian Zhang, Yong Deng
Summary: The importance of research on complex networks is increasing, and identifying influential nodes remains an urgent and crucial issue. This paper proposes a Local Degree Dimension (LDD) approach that assesses the importance of nodes in complex networks by considering the increasing and decreasing rates of the numbers of each layer neighbor nodes. Experimental results demonstrate the effectiveness of LDD in accurately identifying influential nodes and quantifying their importance.
INFORMATION SCIENCES
(2022)
Article
Operations Research & Management Science
Alessandro Chessa, Pierpaolo D'Urso, Livia De Giovanni, Vincenzina Vitale, Alfonso Gebbia
Summary: This paper uses a weighted complex network and a sparsification procedure to detect communities of basketball players. It calculates the best community structure and maximizes modularity as a measure of compactness and separation. The effectiveness of the sparsification transition is confirmed. This method not only finds the best distribution of nodes and number of communities, but also enables a data-driven decision-making process in basketball.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Multidisciplinary Sciences
Zhihao Dong, Yuanzhu Chen, Terrence S. Tricco, Cheng Li, Ting Hu
Summary: The paper proposes a localized strategy to identify vital nodes without global knowledge of the network. Experimental results show that the average degree of the identified node set is 3-8 times higher than that of the full node set, and higher-degree nodes take larger proportions in the degree distribution of the identified vital node set.
SCIENTIFIC REPORTS
(2021)
Article
Physics, Multidisciplinary
Jinhua Zhang, Qishan Zhang, Ling Wu, Jinxin Zhang
Summary: This paper proposes a new multiple local attributes-weighted centrality (LWC) method based on information entropy for identifying influential nodes in complex networks. The method considers the one-step and two-step neighborhood information of nodes and calculates the LWC value of each node by weighting and summing multiple influence measures. Experimental results demonstrate the good performance of the proposed method in terms of discrimination capability and accuracy.
