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Search Results (10)
  • Open Access

    ARTICLE

    Identifying Influential Communities Using IID for a Multilayer Networks

    C. Suganthini*, R. Baskaran

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1715-1731, 2023, DOI:10.32604/iasc.2023.034019

    Abstract In online social networks (OSN), they generate several specific user activities daily, corresponding to the billions of data points shared. However, although users exhibit significant interest in social media, they are uninterested in the content, discussions, or opinions available on certain sites. Therefore, this study aims to identify influential communities and understand user behavior across networks in the information diffusion process. Social media platforms, such as Facebook and Twitter, extract data to analyze the information diffusion process, based on which they cascade information among the individuals in the network. Therefore, this study proposes an influential information diffusion model that identifies… More >

  • Open Access

    ARTICLE

    Social Opinion Network Analytics in Community Based Customer Churn Prediction

    Ayodeji O. J Ibitoye1,*, Olufade F. W Onifade2

    Journal on Big Data, Vol.4, No.2, pp. 87-95, 2022, DOI:10.32604/jbd.2022.024533

    Abstract Community based churn prediction, or the assignment of recognising the influence of a customer’s community in churn prediction has become an important concern for firms in many different industries. While churn prediction until recent times have focused only on transactional dataset (targeted approach), the untargeted approach through product advisement, digital marketing and expressions in customer’s opinion on the social media like Twitter, have not been fully harnessed. Although this data source has become an important influencing factor with lasting impact on churn management. Since Social Network Analysis (SNA) has become a blended approach for churn prediction and management in modern… More >

  • Open Access

    ARTICLE

    Seed-Oriented Local Community Detection Based on Influence Spreading

    Shenglong Wang1,*, Jing Yang1,*, Xiaoyu Ding2, Jianpei Zhang1, Meng Zhao1

    CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.1, pp. 215-249, 2022, DOI:10.32604/cmes.2022.018050

    Abstract In recent years, local community detection algorithms have developed rapidly because of their nearly linear computing time and the convenience of obtaining the local information of real-world networks. However, there are still some issues that need to be further studied. First, there is no local community detection algorithm dedicated to detecting a seed-oriented local community, that is, the local community with the seed as the core. The second and third issues are that the quality of local communities detected by the previous local community detection algorithms are largely dependent on the position of the seed and predefined parameters, respectively. To… More >

  • Open Access

    ARTICLE

    Community Detection Using Jaacard Similarity with SIM-Edge Detection Techniques

    K. Chitra*, A. Tamilarasi

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 327-337, 2023, DOI:10.32604/csse.2023.023920

    Abstract The structure and dynamic nature of real-world networks can be revealed by communities that help in promotion of recommendation systems. Social Media platforms were initially developed for effective communication, but now it is being used widely for extending and to obtain profit among business community. The numerous data generated through these platforms are utilized by many companies that make a huge profit out of it. A giant network of people in social media is grouped together based on their similar properties to form a community. Community detection is recent topic among the research community due to the increase usage of… More >

  • Open Access

    ARTICLE

    Improved Density Peaking Algorithm for Community Detection Based on Graph Representation Learning

    Jiaming Wang2, Xiaolan Xie1,2,*, Xiaochun Cheng3, Yuhan Wang2

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 997-1008, 2022, DOI:10.32604/csse.2022.027005

    Abstract

    There is a large amount of information in the network data that we can exploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection problem. Meanwhile, there is always an unpredictable distribution of class clusters output by graph representation learning. Therefore, we propose an improved density peak clustering algorithm (ILDPC) for the community detection problem, which improves the local density mechanism in the original algorithm and can better accommodate class clusters of different shapes. And we study the… More >

  • Open Access

    ARTICLE

    Graph Transformer for Communities Detection in Social Networks

    G. Naga Chandrika1, Khalid Alnowibet2, K. Sandeep Kautish3, E. Sreenivasa Reddy4, Adel F. Alrasheedi2, Ali Wagdy Mohamed5,6,*

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5707-5720, 2022, DOI:10.32604/cmc.2022.021186

    Abstract Graphs are used in various disciplines such as telecommunication, biological networks, as well as social networks. In large-scale networks, it is challenging to detect the communities by learning the distinct properties of the graph. As deep learning has made contributions in a variety of domains, we try to use deep learning techniques to mine the knowledge from large-scale graph networks. In this paper, we aim to provide a strategy for detecting communities using deep autoencoders and obtain generic neural attention to graphs. The advantages of neural attention are widely seen in the field of NLP and computer vision, which has… More >

  • Open Access

    ARTICLE

    CGraM: Enhanced Algorithm for Community Detection in Social Networks

    Kalaichelvi Nallusamy*, K. S. Easwarakumar

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 749-765, 2022, DOI:10.32604/iasc.2022.020189

    Abstract Community Detection is used to discover a non-trivial organization of the network and to extract the special relations among the nodes which can help in understanding the structure and the function of the networks. However, community detection in social networks is a vast and challenging task, in terms of detected communities accuracy and computational overheads. In this paper, we propose a new algorithm Enhanced Algorithm for Community Detection in Social Networks – CGraM, for community detection using the graph measures eccentricity, harmonic centrality and modularity. First, the centre nodes are identified by using the eccentricity and harmonic centrality, next a… More >

  • Open Access

    ARTICLE

    Advanced Community Identification Model for Social Networks

    Farhan Amin1, Jin-Ghoo Choi2, Gyu Sang Choi2,*

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1687-1707, 2021, DOI:10.32604/cmc.2021.017870

    Abstract Community detection in social networks is a hard problem because of the size, and the need of a deep understanding of network structure and functions. While several methods with significant effort in this direction have been devised, an outstanding open problem is the unknown number of communities, it is generally believed that the role of influential nodes that are surrounded by neighbors is very important. In addition, the similarity among nodes inside the same cluster is greater than among nodes from other clusters. Lately, the global and local methods of community detection have been getting more attention. Therefore, in this… More >

  • Open Access

    ARTICLE

    Community Detection in Aviation Network Based on K-means and Complex Network

    Hang He1,*, Zhenhan Zhao1, Weiwei Luo1, Jinghui Zhang2

    Computer Systems Science and Engineering, Vol.39, No.2, pp. 251-264, 2021, DOI:10.32604/csse.2021.017296

    Abstract With the increasing number of airports and the expansion of their scale, the aviation network has become complex and hierarchical. In order to investigate the complex network characteristics of aviation networks, this paper constructs a Chinese aviation network model and carries out related research based on complex network theory and K-means algorithm. Initially, the P-space model is employed to construct the Chinese aviation network model. Then, complex network indicators such as degree, clustering coefficient, average path length, betweenness and coreness are selected to investigate the complex characteristics and hierarchical features of aviation networks and explore their causes. Secondly, using K-means… More >

  • Open Access

    ARTICLE

    Reconstruction and Optimization of Complex Network Community Structure under Deep Learning and Quantum Ant Colony Optimization Algorithm

    Peng Mei1, Gangyi Ding1, Qiankun Jin1, Fuquan Zhang2,*, Yeh-Cheng Chen3

    Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 159-171, 2021, DOI:10.32604/iasc.2021.012813

    Abstract Community structure is a key component in complex network systems. This paper aims to improve the effectiveness of community detection and community discovery in complex network systems by providing directions for the reconstruction and optimization of community structures to expand the application of intelligent optimization algorithms in community structures. First, deep learning algorithms and ant colony algorithms are used to elaborate the community detection and community discovery in complex networks. Next, we introduce the technology of transfer learning and propose an algorithm of deep self-encoder modeling based on transfer learning (DSEM-TL). The DSEM-TL algorithm’s indicators include normalized mutual information and… More >

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