Geo-Socially Tenuous Group Query
In location-based social networks, it is important to find a specific group/community. Current research has focused on finding dense subgraphs of close relationships between groups. Compared with the dense group/subgraph, there are few studies on tenuous groups. Although the existing work has begun...
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| Formato: | article |
| Lenguaje: | ZH |
| Publicado: |
Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/fed3fd3269c644e28956188d4da9f069 |
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| Sumario: | In location-based social networks, it is important to find a specific group/community. Current research has focused on finding dense subgraphs of close relationships between groups. Compared with the dense group/subgraph, there are few studies on tenuous groups. Although the existing work has begun to study the tenuous population query, geo-socially tenuous group query has not been studied, and location-based services have a lot of demands in real life. Therefore, it becomes valuable to study the geo-socially tenuous group query. Geo-socially tenuous group query is to find a group of users, which not only satisfies a certain sparsity between users (i.e. the social distance between users is greater than [k]), but also minimizes the distance between users and the query location. To address this problem, this paper first proposes a basic processing algorithm based on c-neighbor (baseline), which uses stored c-neighbor information and distance pruning to help obtain query results quickly. However, the basic processing algorithm based on c-neighbor (baseline) uses too much space and the query efficiency is not high when parameter [k>c]. To solve these problems, a query optimization algorithm based on c-neighbor and reverse c-neighbor (ICN) is proposed, which not only utilizes stored c-neighbor information but also reverse c-neighbor information to effectively filter out invalid users and obtain query results quickly. The experimental results and theory show that the proposed two query processing methods are effective and correct. |
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