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BatchedGreedy: A batch processing approach for influence maximization with candidate constraint

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Abstract

Influence maximization (IM) aims to find k seed nodes from social network G to maximize the spread of influence under a given diffusion model. However, in real social marketing activities, only some users are connected with marketing initiators. In addition, not all users are willing to be a seed for a specific marketing activity. These factors restrict the range of nodes that can act as seed nodes. Therefore, we first propose the candidate constrained influence maximization (CCIM) problem. Here, only seeds from a predefined set of candidate nodes are selected. Despite the similarity in definition between IM and CCIM, many state-of-the-art algorithms for IM cannot be directly applied to CCIM . We propose a batch processing approach BatchedGreedy for CCIM, which utilizes the efficiency of bit operation in a computer to estimate the spread of influence of nodes in batches. Furthermore, for the traditional influence maximization(IM) problem, we propose the filtering-based BatchedGreedy (FB-BG) algorithm by incorporating node filtering with the BatchedGreedy approach. From experimental statistics, it is shown that FB-BG not only provides better performance than state-of-the-art algorithms in comparable running time, but is also more scalable to larger networks.

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Notes

  1. A reachable bit set can be implemented by C++ STL or BitSet in Java, and it can also be easily implemented by other computer languages.

References

  1. Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence through a social network. In: KDD, pp 137–146

  2. Leskovec J, Krause A, Guestrin C, Faloutsos C, Van Briesen J, Glance N (2007) Cost-effective outbreak detection in networks. In: KDD, pp 420–429

  3. Borgs C, Brautbar M, Chayes J, Lucier B (2014) Maximizing social influence in nearly optimal time. In: SODA, pp 946–957

  4. Tang Y, Shi Y, Xiao X (2015) Influence maximization in near-linear time:, A martingale approach. In: SIGMOD, pp 1539–1554

  5. Cheng S, Shen H, Huang J, Zhang G, Cheng X (2013) Staticgreedy: Solving the scalability-accuracy dilemma in influence maximization. In: CIKM, pp 509–518

  6. Goyal A, Lu W, Lakshmanan LV (2011) Celf++: Optimizing the greedy algorithm for influence maximization in social networks. In: WWW, pp 47–48

  7. Cheng S, Shen H, Huang J, Chen W, Cheng X (2014) Imrank: Influence maximization via finding self-consistent ranking. In: SIGIR, pp 475–484

  8. Leskovec J, Krevl A (2014) SNAP Datasets: Stanford large network dataset collection, http://snap.stanford.edu/data

  9. Arora A, Galhotra S, Ranu S (2017) Debunking the myths of influence maximization:, An in-depth benchmarking study. In: SIGMOD, pp 651–666

  10. Feng S, Cong G, Khan A, Li X, Liu Y, Chee YM (2018) Inf2vec: Latent representation model for social influence embedding. In: 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, pp 941–952

  11. Galhotra S, Arora A, Roy S (2016) Holistic influence maximization:, Combining scalability and efficiency with opinion-aware models. In: SIGMOD, pp 743–758

  12. Manchanda S, Mittal A, Dhawan A, Medya S, Ranu S, Singh A (2020) Gcomb: Learning budget-constrained combinatorial algorithms over billion-sized graphs. Adv Neural Info Process Syst, 33:20 000–20 011

  13. Chen H, Qiu W, Ou H, An B, Tambe M (2021) Contingency-aware influence maximization: A reinforcement learning approach. In: Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, UAI 2021, Virtual Event, 27-30 July 2021, ser. Proceedings of Machine Learning Research, C. P. de Campos, M. H. Maathuis, and E. Quaeghebeur, Eds., vol 161. AUAI Press, pp 1535–1545

  14. Meirom EA, Maron H, Mannor S, Chechik G (2021) Controlling graph dynamics with reinforcement learning and graph neural networks. In: Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, ser. Proceedings of Machine Learning Research, M. Meila and T. Zhang, Eds., vol 139. PMLR, pp 7565–7577

  15. Caliò A, Tagarelli A (2021) Attribute based diversification of seeds for targeted influence maximization. Inf Sci 546:1273–1305

    Article  MathSciNet  MATH  Google Scholar 

  16. Zhu J, Ghosh S, Wu W (2019) Group influence maximization problem in social networks. IEEE Trans Comput Soc Syst 6(6):1156–1164

    Article  Google Scholar 

  17. Yin X, Hu X, Chen Y, Yuan X, Li B (2021) Signed-pagerank: An efficient influence maximization framework for signed social networks. IEEE Trans Knowl Data Eng 33(5):2208–2222

    Google Scholar 

  18. Shahrouz S, Salehkaleybar S, Hashemi M (2021) Gim: GPU accelerated ris-based influence maximization algorithm. IEEE Trans Parallel Distributed Syst 32(10):2386–2399

    Article  Google Scholar 

  19. Wu G, Gao X, Yan G, Chen G (2021) Parallel greedy algorithm to multiple influence maximization in social network, ACM Trans. Knowl. Discov. Data, vol 15(3), apr

  20. Karypis G, Kumar V (1998) A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J Sci Comput 20(1):359–392

    Article  MathSciNet  MATH  Google Scholar 

  21. Domingos P, Richardson M (2001) Mining the network value of customers. In: KDD, pp 57–66

  22. Min H, Cao J, Yuan T, Liu B (2020) Topic based time-sensitive influence maximization in online social networks. World Wide Web 23(3):1831–1859

    Article  Google Scholar 

  23. Li X, Cheng X, Su S, Sun C (2018) Community-based seeds selection algorithm for location aware influence maximization. Neurocomputing 275:1601–1613

    Article  Google Scholar 

  24. Wang Y, Fan Q, Li Y, Tan K (2017) Real-time influence maximization on dynamic social streams. Proc VLDB Endow 10(7):805–816

    Article  Google Scholar 

Download references

Acknowledgements

This work is financially supported by Shenzhen Science and Technology Program under Grant No. JCYJ20210324132406016 and National Natural Science Foundation of China under Grant No.61732022.

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  1. Harbin Institute of Technology, Shenzhen, China

    Xiaowei Han, Xiaopeng Yao & Hejiao Huang

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  1. Xiaowei Han
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  2. Xiaopeng Yao
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Correspondence to Hejiao Huang.

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Han, X., Yao, X. & Huang, H. BatchedGreedy: A batch processing approach for influence maximization with candidate constraint. Appl Intell 53, 6909–6925 (2023). https://doi.org/10.1007/s10489-022-03854-0

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