Nowadays personalized influence maximization has become a new branch of influence maximization in social network. The existed methods mainly focus on independent cascade model and linear threshold model, in those two models, nodes’ influence forecast depends on the Monte-Carlo simulation. In order to simulate the propagation of influence more efficiently, we introduce heat diffusion model to the study of personalized influence maximization in this paper. The diffusion process of heat is adopted to simulate the influence of information among users. Furthermore, we propose a target’s heat greedy algorithm based on the analysis of the laws of the heat propagation. Experimental results on real datasets show that our proposed algorithm is effective.
W.Chen, Y.Wang and S.Yang, Efficient influence maximization in social networks, in: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, 2009, pp. 199–208. doi:10.1145/1557019.1557047.
2.
W.X.Chen, C.Zhou and J.Wu, Big social network influence maximization via recursively estimating influence spread, Knowledge-Based Systems113(1) (2015), 143–154.
3.
Y.C.Chen, W.Y.Zhu, W.C.Peng, W.C.Lee and S.Y.Lee, CIM: Community-based influence maximization in social networks, ACM Transactions on Intelligent Systems & Technology5(2) (2014), 529–544.
4.
M.Doo and L.Liu, Probabilistic diffusion of social influence with incentives, IEEE Transactions on Services Computing7(3) (2014), 387–400. doi:10.1109/TSC.2014.2310216.
5.
M.Doo and L.Liu, Extracting top-k most influential nodes by activity analysis, in: Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, IEEE, San Francisco, CA, United States, 2015, pp. 227–236.
6.
A.Goyal, W.Lu and L.V.S.Lakshmanan, CELF++: Optimizing the greedy algorithm for influence maximization in social networks, in: Proceedings of the 20th International Conference Companion on World Wide Web, ACM, Hyderabad, India, 2011, pp. 47–48.
7.
A.Goyal, W.Lu and L.V.S.Lakshmanan, SIMPATH: An efficient algorithm for influence maximization under the linear threshold model, in: Proceedings – IEEE International Conference on Data Mining, IEEE, Vancouver, BC, Canada, 2011, pp. 211–220.
8.
J.Guo, P.Zhang and B.Fang, Personalized key propagating users mining based on LT model, Chinese Journal of Computers37(4) (2014), 809–818.
9.
J.Guo, P.Zhang and C.Zhou, Personalized influence maximization on social networks, in: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, ACM, 2013, pp. 199–208. doi:10.1145/2505515.2505571.
10.
Q.Jiang, G.Song, G.Cong, W.Wang, W.Si and K.Xie, Simulated annealing based influence maximization in social networks, in: Proceedings of the National Conference on Artificial Intelligence, AI Access Foundation, San Francisco, CA, United States, 2011, pp. 127–132.
11.
J.Leskovec, A.Krause, C.Guestrin, C.Faloutsos, J.Vanbriesen and N.Glance, Cost-effective outbreak detection in networks, in: Proceeding of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, San Jose, CA, United States, 2007, pp. 420–429.
12.
H.Li, S.S.Bhowmick, A.Sun and J.Cui, Conformity-aware influence maximization in online social networks, Vldb Journal24(1) (2015), 117–141. doi:10.1007/s00778-014-0366-x.
13.
H.Ma, H.Yang and M.R.Lyu, Mining social networks using heat diffusion processes for marketing candidates selection, in: 17th ACM Conference on Information and Knowledge Management, ACM, Napa Valley, CA, United States, 2008, pp. 233–242.
14.
C.Wang, W.Chen and S.Yang, Scalable influence maximization for independent cascade model in large-scale social networks, Data Mining & Knowledge Discovery25(3) (2012), 545–576. doi:10.1007/s10618-012-0262-1.
15.
Z.Yu, C.Wang, J.Bu, X.Wang, Y.Wu and C.Chen, Friend recommendation with content spread enhancement in social networks, Information Sciences309(10) (2015), 102–118. doi:10.1016/j.ins.2015.03.012.
16.
B.Zhang, Z.Qian and Q.Wang, Maximize information coverage algorithm for target market, Chinese Journal of Computers37(4) (2014), 894–904.
