发表机构
Department of Electrical Engineering, Indian Institute of Technology Kanpur; Adobe Systems, Bangalore; School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm（坎普尔印度理工学院电气工程系; Adobe系统公司班加罗尔分部; 斯德哥尔摩皇家理工学院电气与计算机工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对在线社交网络中的影响力增强问题，本文在Friedkin-Johnsen模型下利用网络拓扑性质，通过边修改模拟推荐机制，提出拓扑条件与离散优化近似解，并在随机图上验证了方法的有效性与鲁棒性。
AI 中文摘要
随着在线社交网络成为广告和宣传的主要平台，增强用户的影响力已成为一个重要的研究课题。本文在Friedkin-Johnsen意见动态模型下研究该问题，其中固执代理会影响网络中其他代理的意见。与大多数现有工作不同，我们利用网络的拓扑性质来增加目标固执代理的影响力。具体而言，我们引入了边修改的概念，该概念模拟了社交网络中的推荐机制。首先，我们提出一个基于拓扑的条件，用于识别总能增加目标固执代理影响力的边修改。结果表明，所选边修改的影响对固执程度和交互权重等参数的变化具有鲁棒性。随后，我们构建一个离散优化问题，以确定一组能最大化代理影响力中心性的边修改。我们为该优化问题提出了一种计算高效的近似解。最后，我们在Erdos-Renyi随机图上的Friedkin-Johnsen意见动态中验证了我们方法的有效性。
英文摘要
With the emergence of online social networks as a primary platform for advertising and advocacy, enhancing a user's influence has become of significant interest. In this paper, we investigate this problem under the Friedkin Johnsen opinion dynamics model, wherein stubborn agents influence the opinions of other agents in the network. Unlike most of the existing works, we leverage topological properties of the network to increase the influence of a desired stubborn agent. Specifically, we introduce the notion of edge modification, which mimics the mechanism of recommendations in social networks. First, we present a topology-based condition that identifies edge modifications that always increase the influence of a desired stubborn agent. It is shown that the impact of the chosen edge modifications remains robust to changes in parameters such as stubbornness and the interaction weights. Thereafter, we formulate a discrete optimisation problem to identify a set of edge modifications that maximise the agent's influence centrality. We present a computationally efficient approximate solution to the optimisation problem. Finally, we demonstrate the effectiveness of our approach on the Friedkin-Johnsen opinion dynamics over the Erdos Renyi random graph.
