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- W2912183246 abstract "With rapid development of location-based social network, data mining on location check-in records can extract a variety of valuable information. In this paper, we aim at using check-in records and user influence to discover influential areas in social networks. Firstly, user influence is calculated by eigenvector centrality method after comparing different centrality methods. Then, the areas with high check-in density are discovered by density-based clustering algorithm. Finally, influence of each area is obtained by accumulated influence within a cluster. The results suggest that areas with high check-in quantity not necessarily have high influence in social networks, and that some users with extremely high influence can significantly improve the influence of the check-in area. Knowing different areas' influence can help advertisers to better locate advertisements and provide reference to merchant for site selection." @default.
- W2912183246 created "2019-02-21" @default.
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- W2912183246 date "2018-06-01" @default.
- W2912183246 modified "2023-09-23" @default.
- W2912183246 title "Discovering Influential Areas According to Check-In Records and User Influence in Social Networks" @default.
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- W2912183246 doi "https://doi.org/10.1109/hpcc/smartcity/dss.2018.00193" @default.
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