Geo-Social Group Queries with Minimum Acquaintance Constraint

The prosperity of location-based social networking services enables geo-social group queries for group-based activity planning and marketing. This paper proposes a new family of geo-social group queries with minimum acquaintance constraint (GSGQs), which are more appealing than existing geo-social group queries in terms of producing a cohesive group that guarantees the worst-case acquaintance level. GSGQs, also specified with various spatial constraints, are more complex than conventional spatial queries; particularly, those with a strict $k$NN spatial constraint are proved to be NP-hard. For efficient processing of general GSGQ queries on large location-based social networks, we devise two social-aware index structures, namely SaR-tree and SaR*-tree. The latter features a novel clustering technique that considers both spatial and social factors. Based on SaR-tree and SaR*-tree, efficient algorithms are developed to process various GSGQs. Extensive experiments on real-world Gowalla and Dianping datasets show that our proposed methods substantially outperform the baseline algorithms based on R-tree.Comment: This is the preprint version that is accepted by the Very Large Data Bases Journa

Maximizing Friend-Making Likelihood for Social Activity Organization

The social presence theory in social psychology suggests that computer-mediated online interactions are inferior to face-to-face, in-person interactions. In this paper, we consider the scenarios of organizing in person friend-making social activities via online social networks (OSNs) and formulate a new research problem, namely, Hop-bounded Maximum Group Friending (HMGF), by modeling both existing friendships and the likelihood of new friend making. To find a set of attendees for socialization activities, HMGF is unique and challenging due to the interplay of the group size, the constraint on existing friendships and the objective function on the likelihood of friend making. We prove that HMGF is NP-Hard, and no approximation algorithm exists unless P = NP. We then propose an error-bounded approximation algorithm to efficiently obtain the solutions very close to the optimal solutions. We conduct a user study to validate our problem formulation and per- form extensive experiments on real datasets to demonstrate the efficiency and effectiveness of our proposed algorithm

Finding nearest Neighbor in Geo-Social Query Processing

Recording the region of people using location-acquisition technologies, such as GPS, allows generating life patterns, which associate people to places they habitually visit. Considering life patterns as edges that connect users of a social network to geographical entities on a spatial network, improves the social network, providing an integrated geo-social graph. Queries over such graph excerpt information on users, with respect to their location history, and excerpt information on geographical entities in correspondence with users who normally visit these entities. A repeated type of query in spatial networks (e.g., road networks) is to find the k- nearest neighbors (k-NN) of a given query objects. With these networks, the distances between objects depend on their network connectivity and it is expensive to compute the distances (e.g., shortest paths) between objects. We present the concept of a geo-social graph that is based on life patterns, where users are connected to geographical entities using life-pattern edges more specifically to allow finding a group of users in a Geo-Social network whose members are close to each other both socially and geographically. We proposed a new approach to find the group of k users who are geo-socially attached to each other and satisfy the all the query points. We used the Bottom up pruning technique for effective pruning of geo-social group queries. An important contribution of this work is in illustrating the usefulness and the feasibility of maintaining and querying integrated geo-social graphs

Dissemination and geovisualization of territorial entities\u27 history

This paper describes an innovative solution for geovisualization of the demographic and administrative history of French municipalities named communes in French. This solution allows for the open dissemination of such data. The challenge is to provide a web interface for unskilled users in order to help them understand complex information about the demographic evolution of French territories. Our approach combines interactive thematic spatial and temporal views. We describe our architecture based on open-source technologies and the organization of this imperfect geo-historical information in our spatiotemporal database. Our second contribution concerns the concept of an acquaintance graph that has been used to obtain an efficient design with good performance in our geovisualization website

Efficient computing of radius-bounded κ-cores

© 2018 IEEE. Driven by real-life applications in geo-social networks, in this paper, we investigate the problem of computing the radius-bounded k-cores (RB-k-cores) that aims to find cohesive subgraphs satisfying both social and spatial constraints on large geo-social networks. In particular, we use k-core to ensure the social cohesiveness and we use a radius-bounded circle to restrict the locations of users in a RB-k-core. We explore several algorithmic paradigms to compute RB-k-cores, including a triple vertex-based paradigm, a binary-vertex-based paradigm, and a paradigm utilizing the concept of rotating circles. The rotating circle-based paradigm is further enhanced with several pruning techniques to achieve better efficiency. The experimental studies conducted on both real and synthetic datasets demonstrate that our proposed rotating-circle-based algorithms can compute all RB-k-cores very efficiently. Moreover, it can also be used to compute the minimum-circle-bounded k-core and significantly outperforms the existing techniques for computing the minimum circle-bounded k-core