TY - JOUR
T1 - Location-Aware Influence Blocking Maximization in Social Networks
AU - Zhu, Wenlong
AU - Yang, Wu
AU - Xuan, Shichang
AU - Man, Dapeng
AU - Wang, Wei
AU - Du, Xiaojiang
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2018
Y1 - 2018
N2 - In real social networks, it is often the case that opposite opinions, ideas, products, or innovations are propagating simultaneously. Although the competitive influence problem has been extensively studied, existing works neglect the fact that the location information can play an important role in influence propagation. In this paper, we study the location-aware influence blocking maximization (LIBM) problem, which aims to find a positive seed set to maximize the blocked negative influence for a given query region. In order to overcome low efficiency of the greedy algorithm, we propose two heuristic algorithms LIBM-H and LIBM-C based on the quadtree index and the maximum influence arborescence structure. Experimental results on real-world datasets show that both LIBM-H and LIBM-C are able to achieve a matching blocking effect to the greedy algorithm and often better than other heuristic algorithms, whereas they are several orders of magnitude faster than the greedy algorithm.
AB - In real social networks, it is often the case that opposite opinions, ideas, products, or innovations are propagating simultaneously. Although the competitive influence problem has been extensively studied, existing works neglect the fact that the location information can play an important role in influence propagation. In this paper, we study the location-aware influence blocking maximization (LIBM) problem, which aims to find a positive seed set to maximize the blocked negative influence for a given query region. In order to overcome low efficiency of the greedy algorithm, we propose two heuristic algorithms LIBM-H and LIBM-C based on the quadtree index and the maximum influence arborescence structure. Experimental results on real-world datasets show that both LIBM-H and LIBM-C are able to achieve a matching blocking effect to the greedy algorithm and often better than other heuristic algorithms, whereas they are several orders of magnitude faster than the greedy algorithm.
KW - Location-aware
KW - influence blocking maximization
KW - social networks
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U2 - 10.1109/ACCESS.2018.2876141
DO - 10.1109/ACCESS.2018.2876141
M3 - Article
AN - SCOPUS:85055021072
VL - 6
SP - 61462
EP - 61477
JO - IEEE Access
JF - IEEE Access
M1 - 8492449
ER -