Abstract
The proliferation of GPS-enabled devices has led to the development of location-based social network services such as Facebook, Twitter, and Foursquare. Users of these services not only make new friends but also post various content that contains their location. Although the existing services have continued to improve, they are still weak in handling some situations. If some users want to make a new friend, for example, they could manually search for the potential friends among the acquaintances of their friends by considering both spatial proximity and social closeness one by one. However, conventional studies have insufficiently tackled this problem yet. In this paper, we define a novel type of geo-social query called the k-Nearest ℓ-Close Friends query, which retrieves the k nearest data objects from among the ℓ-hop friends of the query user. We also propose three approaches for processing a kℓ-NCF query: Neighboring Cell Search, Friend-Cell Search, and Personal-Cell Search. In addition, we develop an efficient method of index update for supporting dynamic environments. We conduct a variety of experiments on synthetic and real data sets to evaluate and compare our methods.
| Original language | English |
|---|---|
| Pages (from-to) | 235-256 |
| Number of pages | 22 |
| Journal | Information Sciences |
| Volume | 423 |
| DOIs | |
| Publication status | Published - 2018 Jan 1 |
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Keywords
- Geo-social networks
- Location-based services
- Nearest close friends query
- Spatial databases
ASJC Scopus subject areas
- Control and Systems Engineering
- Theoretical Computer Science
- Software
- Computer Science Applications
- Information Systems and Management
- Artificial Intelligence
Cite this
Nearest close friend search in geo-social networks. / Shim, Changbeom; Kim, Wooil; Heo, Wan; Yi, Sungmin; Chung, Yon Dohn.
In: Information Sciences, Vol. 423, 01.01.2018, p. 235-256.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Nearest close friend search in geo-social networks
AU - Shim, Changbeom
AU - Kim, Wooil
AU - Heo, Wan
AU - Yi, Sungmin
AU - Chung, Yon Dohn
PY - 2018/1/1
Y1 - 2018/1/1
N2 - The proliferation of GPS-enabled devices has led to the development of location-based social network services such as Facebook, Twitter, and Foursquare. Users of these services not only make new friends but also post various content that contains their location. Although the existing services have continued to improve, they are still weak in handling some situations. If some users want to make a new friend, for example, they could manually search for the potential friends among the acquaintances of their friends by considering both spatial proximity and social closeness one by one. However, conventional studies have insufficiently tackled this problem yet. In this paper, we define a novel type of geo-social query called the k-Nearest ℓ-Close Friends query, which retrieves the k nearest data objects from among the ℓ-hop friends of the query user. We also propose three approaches for processing a kℓ-NCF query: Neighboring Cell Search, Friend-Cell Search, and Personal-Cell Search. In addition, we develop an efficient method of index update for supporting dynamic environments. We conduct a variety of experiments on synthetic and real data sets to evaluate and compare our methods.
AB - The proliferation of GPS-enabled devices has led to the development of location-based social network services such as Facebook, Twitter, and Foursquare. Users of these services not only make new friends but also post various content that contains their location. Although the existing services have continued to improve, they are still weak in handling some situations. If some users want to make a new friend, for example, they could manually search for the potential friends among the acquaintances of their friends by considering both spatial proximity and social closeness one by one. However, conventional studies have insufficiently tackled this problem yet. In this paper, we define a novel type of geo-social query called the k-Nearest ℓ-Close Friends query, which retrieves the k nearest data objects from among the ℓ-hop friends of the query user. We also propose three approaches for processing a kℓ-NCF query: Neighboring Cell Search, Friend-Cell Search, and Personal-Cell Search. In addition, we develop an efficient method of index update for supporting dynamic environments. We conduct a variety of experiments on synthetic and real data sets to evaluate and compare our methods.
KW - Geo-social networks
KW - Location-based services
KW - Nearest close friends query
KW - Spatial databases
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U2 - 10.1016/j.ins.2017.09.049
DO - 10.1016/j.ins.2017.09.049
M3 - Article
AN - SCOPUS:85029852659
VL - 423
SP - 235
EP - 256
JO - Information Sciences
JF - Information Sciences
SN - 0020-0255
ER -