| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Sao Paulo, Inst Math & Comp Sci, BR-14560970 Sao Carlos, SP - Brazil
Total Affiliations: 1
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| Document type: | Journal article |
| Source: | THEORETICAL COMPUTER SCIENCE; v. 851, p. 62-76, JAN 6 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
The quality of a location-based social network (LBSN) is mainly related to the granularity of information on the users' location. When LBSN is built using stay points, it presents much more information since GPS logs convey more users' mobility information. However, the main challenge in building LBSN using stay points is to define local-vertices. This problem is known as local-entity resolution. This local-vertices could represent venues with semantic information like parks, restaurants, among others. The most common way to resolve local-entity is by applying clustering algorithms to group nearby stay points into local-vertices. However, in this case, only geographic information is used, which makes it very difficult to separate geographically close venues into distinct local-vertices. This paper addresses this gap and presents a novel approach that uses the coarsening stage of a multilevel optimization scheme to build LBSNs by using stay points. The experimental evaluation carried out indicates that our approach has advantages compared to usual clustering methods to represent real-world features. (C) 2020 Elsevier B.V. All rights reserved. (AU) | |
| FAPESP's process: | 15/14228-9 - Social Network Analysis and Mining |
| Grantee: | Alneu de Andrade Lopes |
| Support Opportunities: | Regular Research Grants |