The real world contains many complex networks, including research networks, social networks, biological networks, and transport networks. Real-world complex networks are unconstrained and can be characterized as undirected and unweighted. Understanding and controlling the process of information propagation in such networks is significant for decision-making activities and has many uses, such as disease control, market advertising, rumor control, and innovation propagation. Identifying the influencers in complex networks is an important activity, as influencers play a key role in spreading information to aid the decision-making process. In this study, we consider the problem of identifying a set of top-N influential nodes for spreading the information in undirected and unweighted networks using the network structure in the absence of domain-specific knowledge. In this study, we propose a novel method that computes the ranking scores of the nodes in the network and considers the influence of other nodes simultaneously when forming the set of top-N influential nodes. The proposed method is different from other methods of identification of influential nodes in the network, in that it takes into consideration the position of the nodes in the network while computing the ranking score, thereby preventing the clustering of important nodes, which hampers the information flow. Experiments are performed using several real-world complex networks to demonstrate the effectiveness of the proposed method.

现实世界包含许多复杂的网络，包括研究网络、社交网络、生物网络和运输网络。现实世界的复杂网络是不受约束的，可以表征为无向和无权重。了解和控制此类网络中的信息传播过程对于决策活动具有重要意义，并且具有多种用途，例如疾病控制、市场广告、谣言控制和创新传播。识别复杂网络中的影响者是一项重要活动，因为影响者在传播信息以帮助决策过程中发挥着关键作用。在本研究中，我们考虑识别一组 top- N 的问题在缺乏特定领域知识的情况下，使用网络结构在无向和未加权网络中传播信息的有影响力的节点。在这项研究中，我们提出了一种新方法，该方法计算网络中节点的排名分数，并在形成前N 个有影响力的节点集时同时考虑其他节点的影响。所提出的方法与网络中其他有影响的节点识别方法不同，它在计算排名分数时考虑了节点在网络中的位置，从而防止了重要节点的聚集，从而阻碍了信息的流动。 . 使用几个真实世界的复杂网络进行实验以证明所提出方法的有效性。