中文摘要：

最小种子集合选取问题（J-MIN-Seed问题）的目标是选择一个种子集合S,在影响传播结束后,它不仅需要影响一定数量的用户（如J个用户）,同时S是最小的集合.虽然该问题得到了广泛的研究,但是现有工作却忽略了一个重要事实,即地理位置信息对于J-MIN-Seed问题是非常重要的.在许多真实的应用中,例如位置敏感的口碑营销,都有地理位置的需求.因此,该文将地理位置因素融入到J-MIN-Seed问题中,提出了位置敏感的J-MINSeed问题,并证明了该问题是NP-hard问题.该问题的一个挑战是如何高效且有效地计算给定区域的影响范围.为了解决这个挑战,该文对现有的树模型进行扩展,设计出一种高效且有效的近似模型.基于此模型,该文首先提出了朴素的贪心算法MS-Greedy.MS-Greedy虽然有近似保证,但其计算量太大.为满足在线查询的需求,该文又提出了另外两种高效的算法Bound-based和Partition-Assembly-based.大量真实数据的实验结果表明：文中算法能有效地解决位置敏感的J-MIN-Seed问题.

英文摘要：

The goal of the smallest seed set selection problem（J-MIN-Seed problem） is to select a seed set S, at the end of the influence spread, it calls for influencing a certain amount of users （for example J） while the size of S is the smallest. Although the problem has been extensively studied, existing works neglected the fact that the location information can play an important role in J-MIN-Seed problem. In many real-world applications, such as location-aware word-of-mouth marketing, have location-aware requirement. Therefore, we integrate the geographical position factor into J-MIN-Seed problem, and we propose the location-aware J-MIN-Seed problem, and prove that it is NP-hard. One challenge in the problem is how to compute the influence spread of the given region effectively and efficiently. To address this challenge, we extend the existing tree model and design an effective and efficient approximate model. Based on the approximate model, we firstly propose a naive greedy algorithm MS-Greedy. Although MS-Greedy has approximate guarantee, its computation is rather large. In order to meet the needs of online query, we then propose another two effective algorithms Bound-based and Partition-Assembly-based. Experimental results on real data show that our algorithms can solve the location-aware J-MIN-Seed problem effectively.