[1]
H. Nguyen and R. Zheng, On budgeted influence maximization in social networks, IEEE Journal on Selected Areas in Communications, vol. 31, no. 6, pp. 1084-1094, 2013.
[2]
X. Yang, H. Steck, and Y. Liu, Circle-based recommendation in online social networks, in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’14), Beijing, China, 2014, pp. 1267-1275.
[3]
D. Kempe, J. Kleinberg, and É. Tardos, Maximizing the spread of influence through a social network, in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’13), Washington, DC, USA, 2003, pp. 137-146.
[4]
J. Tang, X. Tang, and J. Yuan, Profit maximization for viral marketing in online social networks, in Proceedings of the IEEE International Conference on Network Protocols (ICNP’16), Singapore, 2016. pp. 1095-1108.
[5]
N. Buchbinder, M. Feldman, J. Seffi, and R. Schwartz, A tight linear time (1/2)-approximation for unconstrained submodular maximization, SIAM Journal on Computing, vol. 44, no. 5, pp. 1384-1402, 2015.
[6]
M. Feldman and R. Izsak, Constrained monotone function maximization and the supermodular degree, arXiv preprint arXiv: 1407.6328, 2014.
[7]
H. Zheng, N. Wang, and J. Wu, Non-submodularity and approximability: Influence maximization in online social networks, in Proceedings of the IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM’19), Washington, DC, USA, 2019, pp. 1-10.
[8]
A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee, Measurement and analysis of online social networks, in Proceedings of the ACM SIGCOMM Conference on Internet Measurement (IMC’07), San Diego, CA, USA, 2007, pp. 29-42.
[9]
R. Kumar, J. Novak, and A. Tomkins, Structure and evolution of online social networks, in Link Mining: Models, Algorithms, and Applications. New York, NY, USA: Springer, 2010, pp. 337-357.
[10]
L. Lovász, Submodular functions and convexity, in Mathematical Programming the State of the Art, Heidelberg, German: Springer, 1983, pp. 235-257.
[11]
S. Iwata and K. Nagano, Submodular function minimization under covering constraints, in Proceedings of the IEEE Symposium on Foundations of Computer Science (FOCS’09), Atlanta, GA, USA, 2009, pp. 671-680.
[12]
U. Feige, V. S. Mirrokni, and J. Vondrak, Maximizing non-monotone submodular functions, SIAM Journal on Computing, vol. 40, no. 4, pp. 1133-1153, 2011.
[13]
P.-J. Wan, D.-Z. Du, P. Pardalos, and W. Wu, Greedy approximations for minimum submodular cover with submodular cost, in Computational Optimization and Applications, Springer, 2010, pp. 463-474.
[14]
H.-J. Hung, H.-H. Shuai, D.-N. Yang, L.-H. Huang, W.-C. Lee, J. Pei, and M.-S. Chen, When social influence meets item inference, in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’16), San Francisco, CA, USA, 2016, pp. 915-924.
[15]
S. Fujishige and S. Isotani, A submodular function minimization algorithm based on the minimum-norm base, presented at the Fourth Sino-Japanese Optimization Meeting, Tainan, China, 2008.
[16]
M. Sviridenko, J. Vondrák, and J. Ward, Optimal approximation for submodular and supermodular optimization with bounded curvature, Mathematics of Operations Research, vol. 42, no. 4, pp. 1197-1218, 2017.
[17]
S. Dughmi, Algorithmic information structure design: A survey, ACM SIGecom Exchanges, vol. 15, no. 2, pp. 2-24, 2017.
[18]
T. Gradowski and A. Krawiecki, Majority-vote model on scale-free hypergraphs, Acta Physica Polonica A, vol. 127, no. 3A, pp. 1-4, 2015.
[19]
H. Zheng and J. Wu, NSFA: Nested scale-free architecture for scalable publish/subscribe over p2p networks, in Proceedings of the IEEE International Conference on Network Protocols (ICNP’16), Singapore, 2016, pp. 1-10.
[20]
J. Leskovec, J. Kleinberg, and C. Faloutsos, Graph evolution: Densification and shrinking diameters, ACM Transactions on Knowledge Discovery from Data, vol. 1, no. 1, pp. 1-41, 2007.
AI Chat Paper
