Geo-social group queries with minimum acquaintance constraints

Zhu, Qingfu; Hu, Haibo; Xu, Cheng; Xu, Jianliang; Lee, Wang-Chien

doi:10.1007/s00778-017-0473-6

Cited by 69 publications

(42 citation statements)

References 33 publications

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“…Simple graphs Attributed graphs Keyword Location Temporal Influence (weight) Profile k-core [175,46,15,66] (P. 1, 2, 3, 4, 5) [61,58] (P. 6) [60,65,185,221] (P. 7,8,9) [129] (P. 10) [127,128,30,215,21,126] (P. 12,13) [31] (P. 14) k-truss [98,6,101] (P. 15,16) [102] (P. 17) -- [216] (P. 18) k-clique [45,205,195,187] (P. 19,20,21,22) -- [125] Example 2 Let us reconsider the graph G in Fig. 2(a).…”

Section: Metricmentioning

confidence: 99%

A survey of community search over big graphs

et al. 2019

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With the rapid development of information technologies, various big graphs are prevalent in many real applications (e.g., social media and knowledge bases). An important component of these graphs is the network community. Essentially, a community is a group of vertices which are densely connected internally. Community retrieval can be used in many real applications, such as event organization, friend recommendation, and so on. Consequently, how to efficiently find high-quality communities from big graphs is an important research topic in the era of big data. Recently a large group of research works, called community search, have been proposed. They aim to provide efficient solutions for searching high-quality communities from large networks in real-time. Nevertheless, these works focus on different types of graphs and formulate communities in different manners, and thus it is desirable to have a comprehensive review of these works.In this survey, we conduct a thorough review of existing community search works. Moreover, we analyze

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Section: Metricmentioning

confidence: 99%

A survey of community search over big graphs

et al. 2019

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“…Research on finding a set of attendees for activities based on the social tightness among existing friends [5,6,7,8,9] have been reported in the literature. Social-Temporal Group Query [5] checks the available times of attendees to find the social cohesive group with the most suitable activity time.…”

Section: Related Workmentioning

confidence: 99%

“…Social-Temporal Group Query [5] checks the available times of attendees to find the social cohesive group with the most suitable activity time. Geo-Social Group Query [6,7] extracts socially tight groups while considering certain spatial properties. The willingness optimization for social group problem in [8] selects a set of attendees for an activity while maximizing their willingness to participate.…”

Section: Related Workmentioning

confidence: 99%

“…However, there is a gap between existing activity organization and friend recommendation services in OSNs for the scenarios under consideration. Existing activity organization approaches focus on extracting socially cohesive groups from OSNs based on certain cohesive measures, density, diameter, of social networks or other constraints, e.g., time, spatial distance, and interests, of participants [5,6,7,8]. On the other hand, friend recommendation services consider only the existing friendships to recommend potential new friends for an individual (rather than finding a group of people for engaging friend-making).…”

Section: Introductionmentioning

confidence: 99%

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Maximizing Friend-Making Likelihood for Social Activity Organization

Shen

Yang

Lee

et al. 2015

Advances in Knowledge Discovery and Data Mining

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Abstract. The social presence theory in social psychology suggests that computer-mediated online interactions are inferior to face-to-face, inperson 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 = N P . 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 perform extensive experiments on real datasets to demonstrate the efficiency and effectiveness of our proposed algorithm.

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“…Given such a network, socio spatial group queries [7,25,30,33] aim to find the 'best' group against a Point Of Interest (POI) where the users possess social tightness within the group and have spatial closeness to the POI. An example of such queries is to find a group of three members, who are located close to a particular restaurant and socially connected to at least one of the other members, so that the group is competent for a targeted advertisement of a "20% discount for a table of three" offer running at that restaurant.…”

Section: Introductionmentioning

confidence: 99%

The flexible socio spatial group queries

et al. 2018

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A socio spatial group query finds a group of users who possess strong social connections with each other and have the minimum aggregate spatial distance to a meeting point. Existing studies limit to either finding the best group of a fixed size for a single meeting location, or a single group of a fixed size w.r.t. multiple locations. However, it is highly desirable to consider multiple locations in a real-life scenario in order to organize impromptu activities of groups of various sizes. In this paper, we propose Top k Flexible Socio Spatial Group Query (Top k-FSSGQ) to find the top k groups w.r.t. multiple POIs where each group follows the minimum social connectivity constraints. We devise a ranking function to measure the group score by combining social closeness, spatial distance, and group size, which provides the flexibility of choosing groups of different sizes under different constraints. To effectively process the Top k-FSSGQ, we first develop an Exact approach that ensures early termination of the search based on the derived upper bounds. We prove that the problem is NP-hard, hence we first present a heuristic based approximation algorithm to effectively select members in intermediate solution groups based on the social connectivity of the users. Later we design a Fast Approximate approach based on the relaxed social and spatial bounds, and connectivity constraint heuristic. Experimental studies have verified the effectiveness and efficiency of our proposed approaches on real datasets.

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Geo-social group queries with minimum acquaintance constraints

Cited by 69 publications

References 33 publications

A survey of community search over big graphs

A survey of community search over big graphs

Maximizing Friend-Making Likelihood for Social Activity Organization

The flexible socio spatial group queries

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