Jialong Zhang¹, Meijuan Yin^2,*, Yang Pei², Fenlin Liu², Chenyu Wang²

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2095-2115, 2025, DOI:10.32604/cmc.2025.065679 - 29 August 2025

Abstract Identifying influential users in social networks is of great significance in areas such as public opinion monitoring and commercial promotion. Existing identification methods based on Graph Neural Networks (GNNs) often lead to yield inaccurate features of influential users due to neighborhood aggregation, and require a large substantial amount of labeled data for training, making them difficult and challenging to apply in practice. To address this issue, we propose a semi-supervised contrastive learning method for identifying influential users. First, the proposed method constructs positive and negative samples for contrastive learning based on multiple node centrality metrics… More >

Ayodeji O. J Ibitoye^1,*, Olufade F. W Onifade²

Journal on Big Data, Vol.4, No.2, pp. 87-95, 2022, DOI:10.32604/jbd.2022.024533 - 31 October 2022

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… More >

Farhan Amin, Gyu Sang Choi^*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6367-6391, 2022, DOI:10.32604/cmc.2022.029927 - 28 July 2022

Abstract The Internet of Things (IoT) has the potential to be applied to social networks due to innovative characteristics and sophisticated solutions that challenge traditional uses. Social network analysis (SNA) is a good example that has recently gained a lot of scientific attention. It has its roots in social and economic research, as well as the evaluation of network science, such as graph theory. Scientists in this area have subverted predefined theories, offering revolutionary ones regarding interconnected networks, and they have highlighted the mystery of six degrees of separation with confirmation of the small-world phenomenon. The… More >

Farhan Amin¹, Jin-Ghoo Choi², Gyu Sang Choi^2,*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1687-1707, 2021, DOI:10.32604/cmc.2021.017870 - 21 July 2021

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 >

Yan Liu^1,*, Lian Liu¹, Yu Yan², Hao Feng¹, Shichang Ding³

CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1123-1139, 2019, DOI:10.32604/cmc.2019.05619

Abstract Social organizations can be represented by social network because it can mathematically quantify and represent complex interrelated organizational behavior. Exploring the change in dynamic social network is essential for the situation awareness of the corresponding social organization. Social network usually evolves gradually and slightly, which is hard to be noticed. The statistical process control techniques in industry field have been used to distinguish the statistically significant change of social network. But the original method is narrowed due to some limitation on measures. This paper presents a generic framework to address the change detection problem in More >