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Since its introduction in 2003, the influence maximization (IM) problem has drawn significant research attention in the literature.
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D. Kempe, J. M. Kleinberg, and É. Tardos, “Maximizing the spread of influence through a social network,” in KDD , 2003, pp. 137–146
2003
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D. Kempe, J. M. Kleinberg, and É. Tardos, “Influential nodes in a diffusion model for social networks,” in ICALP , 2005, pp. 1127–1138
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T. Carnes, C. Nagarajan, S. M. Wild, and A. van Zuylen, “Maximizing influence in a competitive social network: a follower’s perspective,” in EC , 2007, pp. 351–360
2007
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W. Chen, Y. Wang, and S. Yang, “Efficient influence maximization in social networks,” in KDD , 2009, pp. 199–208
2009
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J. Allen, “Acquiring commonsense knowledge for a cognitive agent,” in Advances in Cognitive Systems, Papers from the 2011 AAAI Fall Symposium, Arlington, Virginia, USA, November 4-6, 2011 , 2011
2011
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A. Goyal, W. Lu, and L. V. S. Lakshmanan, “CELF++: optimizing the greedy algorithm for influence maximization in social networks,” in WWW , 2011, pp. 47–48
2011
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Q. Jiang, G. Song, G. Cong, Y. Wang, W. Si, and K. Xie, “Simulated annealing based influence maximization in social networks,” in AAAI , 2011
2011
Earlier work this paper cites.
A. Goyal, W. Lu, and L. V. S. Lakshmanan, “SIMPATH: an efficient algorithm for influence maximization under the linear threshold model,” in ICDM , 2011, pp. 211–220
2011
Cited alongside, same era.
T. Cao, X. Wu, X. T. Hu, and S. Wang, “Active learning of model parameters for influence maximization,” in ECML PKDD , 2011, pp. 280–295
2011
Cited alongside, same era.
N. Shervashidze, P. Schweitzer, E. J. v. Leeuwen, K. Mehlhorn, and K. M. Borgwardt, “Weisfeiler-lehman graph kernels,” Journal of Machine Learning Research , vol. 12, no. Sep, pp. 2539–2561, 2011
2011
Cited alongside, same era.
C. Lee, X. Xu, and D. Y. Eun, “Beyond random walk and metropolis-hastings samplers: why you should not backtrack for unbiased graph sampling,” in SIGMETRICS . ACM, 2012, pp. 319–330
2012
Cited alongside, same era.
H. Dai, B. Dai, and L. Song, “Discriminative embeddings of latent variable models for structured data,” in ICML , 2016, pp. 2702–2711
2016
Later among the works it cites.
H. T. Nguyen, M. T. Thai, and T. N. Dinh, “Stop-and-stare: Optimal sampling algorithms for viral marketing in billion-scale networks,” in SIGMOD , 2016, pp. 695–710
2016
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A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in KDD . ACM, 2016, pp. 855–864
2016
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D. Wang, P. Cui, and W. Zhu, “Structural deep network embedding,” in KDD , 2016, pp. 1225–1234
2016
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E. B. Khalil, H. Dai, Y. Zhang, B. Dilkina, and L. Song, “Learning combinatorial optimization algorithms over graphs,” in NIPS , 2017, pp. 6351–6361
2017
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2013
Cited alongside, same era.
N. K. Ahmed, J. Neville, and R. R. Kompella, “Network sampling: From static to streaming graphs,” TKDD , vol. 8, no. 2, pp. 7:1–7:56, 2013
2013
Cited alongside, same era.
C. Doerr and N. Blenn, “Metric convergence in social network sampling,” in HotPlanet@SIGCOMM , 2013, pp. 45–50
2013
Cited alongside, same era.
C. Borgs, M. Brautbar, J. T. Chayes, and B. Lucier, “Maximizing social influence in nearly optimal time,” in SODA , 2014, pp. 946–957
2014
Cited alongside, same era.
Y. Tang, X. Xiao, and Y. Shi, “Influence maximization: near-optimal time complexity meets practical efficiency,” in SIGMOD , 2014, pp. 75–86
2014
Cited alongside, same era.
B. Perozzi, R. Al-Rfou, and S. Skiena, “Deepwalk: Online learning of social representations,” in KDD . ACM, 2014, pp. 701–710
2014
Cited alongside, same era.
Y. Tang, Y. Shi, and X. Xiao, “Influence maximization in near-linear time: A martingale approach,” in SIGMOD , 2015, pp. 1539–1554
2015
Cited alongside, same era.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski et al. , “Human-level control through deep reinforcement learning,” Nature , vol. 518, no. 7540, p. 529, 2015
2015
Cited alongside, same era.
K. Huang, S. Wang, G. S. Bevilacqua, X. Xiao, and L. V. S. Lakshmanan, “Revisiting the stop-and-stare algorithms for influence maximization,” PVLDB , vol. 10, no. 9, pp. 913–924, 2017
2017
Later among the works it cites.
S. Vaswani, B. Kveton, Z. Wen, M. Ghavamzadeh, L. V. S. Lakshmanan, and M. Schmidt, “Model-independent online learning for influence maximization,” in ICML , 2017, pp. 3530–3539
2017
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A. Arora, S. Galhotra, and S. Ranu, “Debunking the myths of influence maximization: An in-depth benchmarking study,” in SIGMOD , 2017, pp. 651–666
2017
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2017
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J. Kim, S. Lee, T. Oh, and I. S. Kweon, “Co-domain embedding using deep quadruplet networks for unseen traffic sign recognition,” in AAAI , 2018, pp. 6975–6982
2018
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K. Ali, C. Wang, and Y. Chen, “Boosting reinforcement learning in competitive influence maximization with transfer learning,” in WI , 2018, pp. 395–400
2018
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L. Sun, W. Huang, P. S. Yu, and W. Chen, “Multi-round influence maximization,” in KDD , 2018, pp. 2249–2258
2018
Later among the works it cites.
K. Han, K. Huang, X. Xiao, J. Tang, A. Sun, and X. Tang, “Efficient algorithms for adaptive influence maximization,” PVLDB , vol. 11, no. 9, pp. 1029–1040, 2018
2018
Later among the works it cites.
J. Qiu, J. Tang, H. Ma, Y. Dong, K. Wang, and J. Tang, “Deepinf: Social influence prediction with deep learning,” in KDD , 2018, pp. 2110–2119
2018
Later among the works it cites.