Circle of Friend Query in Geo-Social Networks

Liu, Weimo; Sun, Wei; Chen, Chunan; Huang, Yan; Jing, Yinan; Chen, Kunjie

doi:10.1007/978-3-642-29035-0_9

Cited by 40 publications

(31 citation statements)

References 17 publications

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“…DataSet 4SQ contains both the social and spatial information of 153,577 individuals. Moreover, we also compare MAGS with two relevant algorithms, namely Geo-Social Circle of Friend Query (gCoFQ) [13] and p-Nearest Neighbor (pNN), to evaluate the solution quality and performance. In addition to DataSet 4SQ, we also evaluate MAGS for MRGQ on a large real dataset, DataSet Youtube [21], which is a social network extracted from Youtube video-sharing website with 1,134,890 individuals.…”

Section: Resultsmentioning

confidence: 99%

“…Figure 14 first compares MAGS in MRGQ with the related works on DataSet 4SQ, where MAGS is equipped with APDO, Socio-Spatial Ordering and the proposed pruning strategies. 1) Geo-Social Circle of Friend Query (gCoFQ) [13] aims to find a group of p people to minimize the linear combination of the social diameter and spatial diameter (maximum spatial distance between each pair of group members) of the selected group. In other words, there is no activity location in gCoFQ.…”

Section: Performance Evaluation Of Proposed Algorithms For Mrgqmentioning

confidence: 99%

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Socio-Spatial Group Queries for Impromptu Activity Planning

Shen

Yang

Huang

et al. 2016

IEEE Trans. Knowl. Data Eng.

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The development and integration of social networking services and smartphones have made it easy for individuals to organize impromptu social activities anywhere and anytime. Main challenges arising in organizing impromptu activities are mostly due to the requirements of making timely invitations in accordance with the potential activity locations, corresponding to the locations of and the relationships among the candidate attendees. Various combinations of candidate attendees and activity locations create a large solution space. Thus, in this paper, we propose Multiple Rally-Point Social Spatial Group Query (MRGQ), to select an appropriate activity location for a group of nearby attendees with tight social relationships. We first consider a special case of MRGQ, namely the Socio-Spatial Group Query (SSGQ), to determine a set of socially acquainted attendees while minimizing the total spatial distance to a specific activity location. We prove that SSGQ is NP-hard and formulate an Integer Linear Programming optimization model for SSGQ. We then develop an efficient algorithm, called SSGS, which employs effective pruning techniques to reduce the running time to determine the optimal solution. Moreover, we propose a heuristic algorithm for SSGQ to efficiently produce good solutions. We next consider the more general MRGQ. Although MRGQ is NP-hard, the number of attendees in practice is usually small enough such that an optimal solution can be found efficiently. Therefore, we first propose an Integer Linear Programming optimization model for MRGQ. We then design an efficient algorithm, called MAGS, which employs effective search space exploration and pruning strategies to reduce the running time for finding the optimal solution. We also propose to further optimize efficiency by indexing the potential activity locations. A user study demonstrates the strength of using SSGS and MAGS over manual coordination in terms of both solution quality and efficiency. Experimental results on real datasets show that our algorithms can process SSGQ and MRGQ efficiently and significantly outperform other baseline algorithms, including one based on the commercial parallel optimizer IBM CPLEX.

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Section: Resultsmentioning

confidence: 99%

Section: Performance Evaluation Of Proposed Algorithms For Mrgqmentioning

confidence: 99%

Socio-Spatial Group Queries for Impromptu Activity Planning

Shen

Yang

Huang

et al. 2016

IEEE Trans. Knowl. Data Eng.

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“…The nearest star group query contained in the framework returns the k nearest subgraphs of m users. In [4], Liu et al proposed propose the k -Geo-Social Circle of Friend Query which aims to finds the group g of k + 1 users, which (i) is connected, (ii) contains u, and (iii) minimizes the maximum distance between any two of its members. In [6], Scellato et al proposed three more geo-social networks metrics: (i) average distance (ii) distance strength, and (iii) average triangle length.…”

Section: Related Workmentioning

confidence: 99%

HOC-Tree: A Novel Index for Efficient Spatio-Temporal Range Search

Long

Zhu

Zhang

et al. 2018

Lecture Notes in Computer Science

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With the rapid development of mobile computing and Web services, a huge amount of data with spatial and temporal information have been collected everyday by smart mobile terminals, in which an object is described by its spatial information and temporal information. Motivated by the significance of spatio-temporal range search and the lack of efficient search algorithm, in this paper, we study the problem of spatio-temporal range search (STRS), a novel index structure is proposed, called HOC-Tree, which is based on Hilbert curve and OC-Tree, and takes both spatial and temporal information into consideration. Based on HOC-Tree, we develop an efficient algorithm to solve the problem of spatio-temporal range search. Comprehensive experiments on real and synthetic data demonstrate that our method is more efficient than the state-of-the-art technique.

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“…It is defined to be the diameter of S ∪ {q}. In fact, diameter-related cost functions have been commonly adopted in graph databases [1,13,2,15] and spatial databases [25,26,27]. To the best of our knowledge, we are the first to study this cost function for CoSKQ.…”

Section: Introductionmentioning

confidence: 99%

Collective spatial keyword queries

Long

Wong

Wang

et al. 2013

Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data

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With the proliferation of spatial-textual data such as location-based services and geo-tagged websites, spatial keyword queries are ubiquitous in real life. One example of spatial-keyword query is the so-called collective spatial keyword query (CoSKQ) which is to find for a given query consisting a query location and several query keywords a set of objects which covers the query keywords collectively and has the smallest cost wrt the query location. In the literature, many different functions were proposed for defining the cost and correspondingly, many different approaches were developed for the CoSKQ problem. In this paper, we study the CoSKQ problem systematically by proposing a unified cost function and a unified approach for the CoSKQ problem (with the unified cost function). The unified cost function includes all existing cost functions as special cases and the unified approach solves the CoSKQ problem with the unified cost function in a unified way. Experiments were conducted on both real and synthetic datasets which verified our proposed approach.

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Circle of Friend Query in Geo-Social Networks

Cited by 40 publications

References 17 publications

Socio-Spatial Group Queries for Impromptu Activity Planning

Socio-Spatial Group Queries for Impromptu Activity Planning

HOC-Tree: A Novel Index for Efficient Spatio-Temporal Range Search

Collective spatial keyword queries

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