Location-Interest-Aware Community Detection for Mobile Social Networks Based on Auto Encoder
| Autor: | Wenzhong Li, Ming Chen, Daoxu Chen, Sanglu Lu |
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| Rok vydání: | 2019 |
| Předmět: |
Topic model
Social network business.industry Computer science 02 engineering and technology Machine learning computer.software_genre Autoencoder Variety (cybernetics) Precision marketing Mobile social network 020204 information systems Location-based service 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Baseline (configuration management) computer |
| Zdroj: | Knowledge Science, Engineering and Management ISBN: 9783030295509 KSEM (1) |
| DOI: | 10.1007/978-3-030-29551-6_16 |
| Popis: | Community detection partitions users in social networks into sub-groups according to structural or behavioral similarities, which had been widely adopted by a lot of applications such as friend recommendation, precision marketing, etc. In this paper, we propose a location-interest-aware community detection approach for mobile social networks. Specifically, we develop a spatial-temporal topic model to describe users’ location interest, and introduce an auto encoder mechanism to represent users’ location features and social network features as low-dimensional vectors, based on which a community detection algorithm is applied to divide users into sub-graphs. We conduct extensive experiments based on a real-world mobile social network dataset, which demonstrate that the proposed community detection approach outperforms the baseline algorithms in a variety of performance metrics. |
| Databáze: | OpenAIRE |
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