Rashidi Rohollah, Boroujeni Farsad Zamani, Soltanaghaei MohammadReza, Farhadi Hadi
Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.
Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Sci Rep. 2024 Jul 9;14(1):15815. doi: 10.1038/s41598-024-66277-6.
Identifying influential nodes is one of the basic issues in managing large social networks. Identifying influence nodes in social networks and other networks, including transportation, can be effective in applications such as identifying the sources of spreading rumors, making advertisements more effective, predicting traffic, predicting diseases, etc. Therefore, it will be important to identify these people and nodes in social networks from different aspects. In this article, a new method is presented to identify influential nodes in the social network. The proposed method utilizes the combination of users' social characteristics and their reaction information to identify influential users. Since the identification of these users in the large social network is a complex process and requires high processing power and time, clustering and identifying communities have been used in the proposed method to reduce the complexity of the problem. In the proposed method, the structure of the social network is divided into its constituent communities and thus the problem of identifying influential nodes (in the entire network) turns into several problems of identifying an influential node (in each community). The suggested method for predicting the nodes first predicts the links that may be created in the future and then identifies the influential nodes based on an iterative strategy. The proposed algorithm uses the criteria of centrality and influence domain to identify this category of users and performs the identification process both at the community and network levels. The efficiency of the method has been evaluated using real databases and the results have been compared with previous works. The results demonstrate that the proposed method provides a more suitable performance in detecting the influential nodes and is superior in terms of accuracy, recall and processing time.
识别有影响力的节点是管理大型社交网络的基本问题之一。在社交网络以及包括交通网络在内的其他网络中识别有影响力的节点，在诸如识别谣言传播源头、提高广告效果、预测交通流量、预测疾病等应用中可能会很有效。因此，从不同方面识别社交网络中的这些人和节点将很重要。在本文中，提出了一种识别社交网络中有影响力节点的新方法。所提出的方法利用用户的社会特征及其反应信息的组合来识别有影响力的用户。由于在大型社交网络中识别这些用户是一个复杂的过程，需要高处理能力和时间，因此在所提出的方法中使用了聚类和识别社区的方法来降低问题的复杂性。在所提出的方法中，社交网络的结构被划分为其组成社区，因此识别有影响力节点（在整个网络中）的问题就变成了几个识别有影响力节点（在每个社区中）的问题。所建议的预测节点的方法首先预测未来可能创建的链接，然后基于迭代策略识别有影响力的节点。所提出的算法使用中心性和影响域标准来识别这类用户，并在社区和网络层面都执行识别过程。已使用真实数据库评估了该方法的效率，并将结果与以前的工作进行了比较。结果表明，所提出的方法在检测有影响力的节点方面提供了更合适的性能，并且在准确性、召回率和处理时间方面更具优势。
