基于k壳的HookeRank算法识别加权网络中的影响节点
Identifying Influential Nodes in Weighted Networks using k-shell based HookeRank Algorithm
摘要：
寻找有影响力的传播者是网络分析领域的一项重要任务，因为它具有许多理论和实践意义。这些节点在信息扩散过程中发挥着至关重要的作用，就像病毒式营销一样。许多现实生活中的网络都是加权网络，但与未加权网络相比，在加权网络中找到有影响力的节点的工作相对较少。在本文中，我们提出了一个基于k壳的HookeRank（KSHR）算法来识别加权网络中的扩散器。首先，我们提出了加权k壳中心节点u通过使用$u$的k壳值，其邻居的k壳值（$v$），以及它们之间的边权重（$w_{uv}$）。我们将网络中的边建模为弹簧，边权重为弹簧常数。基于胡克弹性定律的概念，我们假设一个力等于加权k壳值作用在每个节点上。在这种安排中，我们制定了KSHR中心的每个节点使用相关的加权k壳值和等效的边的权重，通过照顾从源节点的边缘的串联和并联组合，最多3跳邻居。该算法找到有影响力的节点，可以传播信息的最大数量的节点在网络中。我们比较我们提出的算法与流行的现有算法，并观察到它优于他们在许多现实生活中的和合成的网络使用易感性感染的传播（SIR）信息扩散模型。
英文摘要：
Finding influential spreaders is a crucial task in the field of network analysis because of numerous theoretical and practical importance. These nodes play vital roles in the information diffusion process, like viral marketing. Many real-life networks are weighted networks, but relatively less work has been done for finding influential nodes in the case of weighted networks as compared to unweighted networks. In this paper, we propose a k-shell-based HookeRank (KSHR) algorithm to identify spreaders in weighted networks. First, we propose weighted k-shell centrality of the node u by using the k-shell value of $u$, the k-shell value of its neighbors ($v$), and edge weight ($w_{uv}$) between them. We model edges present in the network as springs and edge weights as spring constants. Based on the notion of Hooke's law of elasticity, we assume a force equal to the weighted k-shell value acts on each node. In this arrangement, we formulate the KSHR centrality of each node using associated weighted k-shell value and the equivalent edge weight by taking care of series and parallel combination of edges up to 3-hop neighbors from the source node. The proposed algorithm finds influential nodes that can spread the information to the maximum number of nodes in the network. We compare our proposed algorithm with popular existing algorithms and observe that it outperforms them on many real-life and synthetic networks suing Susceptible-Infected-Recovered (SIR) information diffusion model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
