Users in social networks often form relationships with other users who participate together in various activities nearby. The activity locations which are frequently shared with the friends are important in real life in order to understand the precise spatial space of the social users. However, the locations of individuals in a social network are often unknown. This is because the social users do not bother to broadcast their locations in public due to many reasons including privacy. Identifying the top activity location of a user at a higher granularity level will improve various community based applications like Meetup, Groupon, etc. In this paper, we propose a method to infer the top activity location of social users using the implicit information available in the network. Our proposed approach can estimate the activity location of a user by propagating the spatial information of the neighbors through friendship edges. We maintain a proper inference sequence to propagate the location labels of the users. We find that the proposed method has significantly improved the state-of-the-art network based location inference techniques in terms of both the accuracy and efficiency.

社交网络中的用户通常与附近一起参与各种活动的其他用户形成关系。经常与朋友分享的活动位置在现实生活中很重要，以便了解社交用户的精确空间空间。然而，个人在社交网络中的位置通常是未知的。这是因为由于包括隐私在内的许多原因，社交用户不会在公共场合广播他们的位置。在更高的粒度级别识别用户的热门活动位置将改进各种基于社区的应用程序，如 Meetup、Groupon 等。在本文中，我们提出了一种使用隐式信息来推断社交用户的热门活动位置的方法。网络。我们提出的方法可以通过朋友边缘传播邻居的空间信息来估计用户的活动位置。我们维护一个适当的推理序列来传播用户的位置标签。我们发现所提出的方法在准确性和效率方面都显着提高了基于网络的最先进的位置推断技术。