论文标题
CKS：一种基于社区的K-shell分解方法，使用社区桥接节点来影响最大化
CKS: A Community-based K-shell Decomposition Approach using Community Bridge Nodes for Influence Maximization
论文作者
论文摘要
社交网络已在其平台上启用了特定于用户的广告和建议，这将重点放在目标广告和相关任务的影响力最大化（IM）上。目的是识别网络中的节点，该节点可以通过扩散级联反应最大化信息的传播。我们提出了一种基于社区结构的方法，该方法采用K-shell算法和社区结构来为种子节点与社区之间的联系产生分数。此外，我们的方法采用社区内部的熵来确保信息在社区内的适当传播。我们验证了四个公开可用网络的方法，并显示出对四种最先进方法的优势，同时仍然相对高效。
Social networks have enabled user-specific advertisements and recommendations on their platforms, which puts a significant focus on Influence Maximisation (IM) for target advertising and related tasks. The aim is to identify nodes in the network which can maximize the spread of information through a diffusion cascade. We propose a community structures-based approach that employs K-Shell algorithm with community structures to generate a score for the connections between seed nodes and communities. Further, our approach employs entropy within communities to ensure the proper spread of information within the communities. We validate our approach on four publicly available networks and show its superiority to four state-of-the-art approaches while still being relatively efficient.
