TY - JOUR
T1 - Efficient Similarity-Aware Influence Maximization in Geo-Social Network
AU - Chen, Xuanhao
AU - Zhao, Yan
AU - Liu, Guanfeng
AU - Sun, Rui
AU - Zhou, Xiaofang
AU - Zheng, Kai
N1 - Publisher Copyright:
IEEE
PY - 2022/12/1
Y1 - 2022/12/1
N2 - 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 to maximize the expected spread of influence in the networks, has drawn increasing attention. However, most recent work tries to study influence maximization by only considering geographic distance, while ignoring the influence of users' spatio-temporal behavior on information propagation or location promotion, which can often lead to poor results. To relieve this problem, we propose a Similarity-aware Influence Maximization (SIM) model to efficiently maximize the influence spread by taking the effect of users' spatio-temporal behavior into account, which is more reasonable to describe the real information propagation. We first calculate the similarity between users according to their historical check-ins, and then we propose a Propagation to Consumption (PTC) model to capture both online and offline behaviors of users. Finally, we propose two greedy algorithms to efficiently maximize the influence spread. The extensive experiments over real datasets demonstrate the efficiency and effectiveness of the proposed algorithms.
AB - 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 to maximize the expected spread of influence in the networks, has drawn increasing attention. However, most recent work tries to study influence maximization by only considering geographic distance, while ignoring the influence of users' spatio-temporal behavior on information propagation or location promotion, which can often lead to poor results. To relieve this problem, we propose a Similarity-aware Influence Maximization (SIM) model to efficiently maximize the influence spread by taking the effect of users' spatio-temporal behavior into account, which is more reasonable to describe the real information propagation. We first calculate the similarity between users according to their historical check-ins, and then we propose a Propagation to Consumption (PTC) model to capture both online and offline behaviors of users. Finally, we propose two greedy algorithms to efficiently maximize the influence spread. The extensive experiments over real datasets demonstrate the efficiency and effectiveness of the proposed algorithms.
KW - Geo-social networks
KW - Influence maximization
KW - Mathematical model
KW - Measurement
KW - Probability
KW - Q measurement
KW - Similarity-aware
KW - Social networking (online)
KW - Sun
KW - Upper bound
UR - http://www.scopus.com/inward/record.url?scp=85098766495&partnerID=8YFLogxK
U2 - 10.1109/TKDE.2020.3045783
DO - 10.1109/TKDE.2020.3045783
M3 - Journal article
AN - SCOPUS:85098766495
SN - 1041-4347
VL - 34
SP - 4767
EP - 4780
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 10
ER -