Efficient Similarity-Aware Influence Maximization in Geo-Social Network
نویسندگان
چکیده
With the explosion of GPS-enabled smartphones and social media platforms, geo-social networks are increasing as tools for businesses to promote their products or services. Influence maximization, which aims maximize expected spread influence in networks, has drawn attention. However, most recent work tries study maximization by only considering geographic distance, while ignoring users’ spatio-temporal behavior on information propagation location promotion, can often lead poor results. To relieve this problem, we propose a Similarity-aware Maximization (SIM) model efficiently taking effect into account, is more reasonable describe real propagation. We first calculate similarity between users according historical check-ins, then Propagation Consumption (PTC) capture both online offline behaviors users. Finally, two greedy algorithms spread. The extensive experiments over datasets demonstrate efficiency effectiveness proposed algorithms.
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ژورنال
عنوان ژورنال: IEEE Transactions on Knowledge and Data Engineering
سال: 2022
ISSN: ['1558-2191', '1041-4347', '2326-3865']
DOI: https://doi.org/10.1109/tkde.2020.3045783