The online social network has become an integral part of our day to life and serves as an excellent platform for sharing ideas, opinions, and products. Influence maximization (IM) is a widely studied topic in the area of social network analysis. The objective of IM is to find influential nodes that can disseminate information to a larger extent in the network. Many local and global centrality measures are proposed to rank the nodes based on their spreading capability with certain limitations. Many proposed algorithms locate the spreaders sharing overlapping regions or are closely placed, which may cause interference in spreading. In this paper, based on the notion of maximum coverage of the information and minimum interference in spreading, we propose a novel semi-local algorithm named as modified degree centrality with exclusion ratio to identify influential nodes from diverse locations in the network. We use modified degree centrality by considering neighbours upto 2-hops and introduce the novel idea of exclusion ratio to ensure minimum overlapping between regions influenced by the chosen spreader nodes. Extensive experimental validation using classical information diffusion model demonstrates that the proposed method delivers better results in comparison to many popular contemporary methods of influence maximization.

在线社交网络已成为我们日常生活中不可或缺的一部分，是分享想法、观点和产品的绝佳平台。影响最大化 (IM) 是社交网络分析领域中一个被广泛研究的主题。IM 的目标是找到能够在网络中更大范围传播信息的有影响力的节点。提出了许多局部和全局中心性措施来根据节点的传播能力对节点进行排名，但具有一定的局限性。许多提出的算法定位共享重叠区域或靠近放置的扩展器，这可能会导致扩展干扰。在本文中，基于信息的最大覆盖和传播中的最小干扰的概念，我们提出了一种新的半局部算法，命名为使用排除率修改度中心性，以识别来自网络中不同位置的有影响的节点。我们通过考虑最多 2 跳的邻居来使用修改的度中心性，并引入排除比的新思想，以确保受所选传播器节点影响的区域之间的重叠最小。使用经典信息扩散模型的广泛实验验证表明，与许多流行的当代影响最大化方法相比，所提出的方法提供了更好的结果。