Given a social network, where each user is associated with a selection cost, the problem of \textsc{Budgeted Influence Maximization} (\emph{BIM Problem} in short) asks to choose a subset of them (known as seed users) within an allocated budget whose initial activation leads to the maximum number of influenced nodes. Existing Studies on this problem do not consider the tag-specific influence probability. However, in reality, influence probability between two users always depends upon the context (e.g., sports, politics, etc.). To address this issue, in this paper we introduce the \textsc{Tag\mbox{-}Based Budgeted Influence Maximization problem} (\emph{TBIM Problem} in short), where along with the other inputs, a tag set (each of them is also associated with a selection cost) is given, each edge of the network has the tag specific influence probability, and here the goal is to select influential users as well as influential tags within the allocated budget to maximize the influence. Considering the fact that real-world campaigns targeted in nature, we also study the \textsc{Earned Benefit Maximization} Problem in tag specific influence probability setting, which formally we call the \textsc{Tag\mbox{-}Based Earned Benefit Maximization problem} (\emph{TEBM Problem} in short). For this problem along with the inputs of the TBIM Problem, we are given a subset of the nodes as target users, and each one of them is associated with a benefit value that can be earned by influencing them. Considering the fact that different tag has different popularity across the communities of the same network, we propose three methodologies that work based on \emph{effective marginal influence gain computation}. The proposed methodologies have been analyzed for their time and space requirements.

中文翻译：

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社交网络中带有标签的预算影响力和收益最大化

在给定一个社交网络的情况下，每个用户都与一个选择成本相关联，\ textsc {预算影响最大化}（简称\ emph {BIM问题}）的问题要求选择一个用户中的一个子集（称为种子用户）已分配的预算，其初始激活会导致受影响的节点数达到最大。关于这个问题的现有研究没有考虑标签特有的影响概率。但是，实际上，两个用户之间的影响概率始终取决于上下文（例如，体育，政治等）。为了解决这个问题，在本文中，我们介绍了\ textsc {Tag \ mbox {-}基于预算的影响力最大化问题}（简称\ emph {TBIM问题}），其中包括其他输入，标签集（每个标签集它们也与选择成本相关联），网络的每个边缘都具有标签特定的影响概率，此处的目标是在分配的预算内选择有影响力的用户以及有影响力的标签，以最大程度地发挥影响力。考虑到现实世界中的活动本质上是针对性的，我们还研究了标签特定影响概率设置中的\ textsc {Earned Benefit Maximization}问题，我们正式将其称为\ textsc {Tag \ mbox {-} Based Earned Benefit Maximization问题}（简称\ emph {TEBM Problem}）。对于该问题以及TBIM问题的输入，我们将节点的一个子集作为目标用户，并且将每个节点与可通过影响它们而获得的收益值相关联。考虑到以下事实：不同的标记在同一网络的社区中具有不同的知名度，我们提出了三种基于\ emph {有效边际影响增益计算}的方法。已对所提出的方法进行了时间和空间要求的分析。