研究人员开发了一种名为BARE的新方法来解决图元组问题，旨在以最少的信息请求来识别网络中最具影响力的节点。该方法特别适用于社交网络营销，其目标是找到并利用关键客户。与先前需要部分或全部图知识的方法不同，BARE在没有任何先验信息的情况下运行，并主动地按顺序发现图。所提出的策略提供了与可检测维度成比例的遗憾保证，该维度通常小于节点总数。 AI
影响 引入了一种用于未知网络中影响最大化的新颖算法，有可能改进定向营销策略。
排序理由 该集群包含一篇详细介绍图元组问题新算法的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2605.00489v1 Announce Type: new Abstract: We study a graph bandit setting where the objective of the learner is to detect the most influential node of a graph by requesting as little information from the graph as possible. One of the relevant applications for this setting i…
We study a graph bandit setting where the objective of the learner is to detect the most influential node of a graph by requesting as little information from the graph as possible. One of the relevant applications for this setting is marketing in social networks, where the market…