Wang Guan, Alias Syazwina Binti, Sun Zejun, Wang Feifei, Fan Aiwan, Hu Haifeng
School of Information Engineering, Pingdingshan University, Pingdingshan, Henan, China.
Faculty of Engineering, Built Environment & Information Technology, SEGI University, Malaysia.
Heliyon. 2023 May 10;9(5):e16112. doi: 10.1016/j.heliyon.2023.e16112. eCollection 2023 May.
Influential nodes identification technology is one of the important topics which has been widely applied to logistics node location, social information dissemination, transportation network carrying, biological virus dissemination, power network anti-destruction, etc. At present, a large number of influential nodes identification methods have been studied, but the algorithms that are simple to execute, have high accuracy and can be better applied to real networks are still the focus of research. Therefore, due to the advantages of simple to execute in voting mechanism, a novel algorithm based on adaptive adjustment of voting ability (AAVA) to identify the influential nodes is presented by considering the local attributes of node and the voting contribution of its neighbor nodes, to solve the problem of low accuracy and discrimination of the existing algorithms. This proposed algorithm uses the similarity between the voting node and the voted node to dynamically adjust its voting ability without setting any parameters, so that a node can contribute different voting abilities to different neighbor nodes. To verify the performance of AAVA algorithm, the running results of 13 algorithms are analyzed and compared on 10 different networks with the SIR model as a reference. The experimental results show that the influential nodes identified by AAVA have high consistency with SIR model in Top-10 nodes and Kendall correlation, and have better infection effect of the network. Therefore, it is proved that AAV algorithm has high accuracy and effectiveness, and can be applied to real complex networks of different types and sizes.
影响力节点识别技术是重要研究课题之一，已广泛应用于物流节点选址、社会信息传播、交通网络承载、生物病毒传播、电网抗破坏等领域。目前，已有大量影响力节点识别方法被研究，但执行简单、精度高且能更好应用于实际网络的算法仍是研究重点。因此，鉴于投票机制执行简单的优点，通过考虑节点局部属性及其邻居节点的投票贡献，提出一种基于投票能力自适应调整（AAVA）的新型影响力节点识别算法，以解决现有算法精度低和区分度差的问题。该算法利用投票节点与被投票节点之间的相似度动态调整投票能力，无需设置任何参数，使得一个节点能对不同邻居节点贡献不同的投票能力。为验证AAVA算法性能，以SIR模型为参照，在10个不同网络上分析比较了13种算法的运行结果。实验结果表明，AAVA识别出的影响力节点在Top-10节点和肯德尔相关性方面与SIR模型具有高度一致性，且对网络具有更好的感染效果。因此，证明AAV算法具有高精度和有效性，可应用于不同类型和规模的实际复杂网络。
