Finding influential nodes reshuffles the very notion of linear paths in business processes and replaces it with networks of business value within a smart enterprise system. There are many existing algorithms for identifying influential nodes with certain limitations for applying in large-scale networks. In this article, we propose a community structure with integrated features ranking (CIFR) algorithm to find influential nodes in the network. First, we use the community detection algorithm to find communities in the system, and then we rank the nodes of network based on three factors, namely local ranking, gateway ranking, and community ranking, collectively termed as integrated features. Our algorithm intends to select influential nodes, which are both globally and locally optimal, leading to overall high information propagation. We perform the experimental results on total eight networks using various evaluation parameters. The obtained results validate superior performance against contemporary algorithms adding value to smart enterprises.

中文翻译：

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使用集成特征排名的社区结构识别智能企业的影响节点

寻找有影响力的节点改变了业务流程中线性路径的概念，并用智能企业系统中的业务价值网络取而代之。存在许多用于识别影响节点的现有算法，但对于在大规模网络中的应用具有一定的局限性。在本文中，我们提出了一种具有集成特征排名（CIFR）算法的社区结构，以在网络中寻找有影响力的节点。首先，我们使用社区检测算法在系统中发现社区，然后根据本地排名、网关排名和社区排名三个因素对网络节点进行排名，统称为综合特征。我们的算法旨在选择具有影响力的节点，这些节点是全局和局部最优的，从而导致整体的高信息传播。我们使用各种评估参数在总共八个网络上执行实验结果。获得的结果验证了与当代算法相比的卓越性能，为智能企业增加了价值。