论文标题
MCD：一种改良的社区多样性方法，用于检测社交网络中有影响力的节点
MCD: A Modified Community Diversity Approach for Detecting Influential Nodes in Social Networks
论文作者
论文摘要
在过去的几十年中，社交网络已将网络上的人们连接到全球的网络上，并已成为我们日常生活的关键部分。这些网络还迅速发展为传播产品，想法和观点以针对更广泛受众的平台。这要求有必要出于多种原因在网络中找到有影响力的节点，包括分布在网络之间的错误信息的遏制，有效地广告产品，在生物网络中找到了突出的蛋白质结构等。在本文中，我们提出了修改后的社区多样性（MCD），一种通过在网络中找到有影响力的社区探索的新方法，方法是通过剥夺社区的多样性和一种改良的多样性。我们将社区多样性的概念扩展到两个跳跃的情况。这有助于我们更准确地评估节点对网络的可能影响，并避免选择具有重叠影响范围的种子节点。实验结果验证了MCD在三个性能指标上累积八个数据集上的其他各种最先进的方法。
Over the last couple of decades, Social Networks have connected people on the web from across the globe and have become a crucial part of our daily life. These networks have also rapidly grown as platforms for propagating products, ideas, and opinions to target a wider audience. This calls for the need to find influential nodes in a network for a variety of reasons, including the curb of misinformation being spread across the networks, advertising products efficiently, finding prominent protein structures in biological networks, etc. In this paper, we propose Modified Community Diversity (MCD), a novel method for finding influential nodes in a network by exploiting community detection and a modified community diversity approach. We extend the concept of community diversity to a two-hop scenario. This helps us evaluate a node's possible influence over a network more accurately and also avoids the selection of seed nodes with an overlapping scope of influence. Experimental results verify that MCD outperforms various other state-of-the-art approaches on eight datasets cumulatively across three performance metrics.
