Abstract
Influence Maximization is a significant problem aimed to find a set of seed nodes to maximize the spread of given events in social networks. Previous studies are contributing to the efficiency and online dynamics of basic IM on classical graph structure. However, they lack an adequate consideration of individual and group behavior on propagation probability. This can be attributed to inadequate attention given to node Individual Treatment Effects (ITE), which significantly impacts the probability of propagation by dividing the sensitive attributes of nodes. Additionally, current research lacks exploration of temporal constraints in influence spreading process under higher order interference on hypergraphs. To fill these two gaps, we introduce two sets of basic assumptions about the impact of ITE on the propagation process and develop a new diffusion model called the Latency Aware Contact Process on Causal Independent Cascading (LT-CPCIC) under time constraints on hypergraphs. We further design Causal-Inspired Cost-Effective Balanced Selection algorithm (CICEB) for the proposed models. CICEB first recovers node ITE from observational data and applies three types of debiasing strategies, namely DebiasFormer, DebiasCur and DebiasInteg, to weaken the correlation between the propagation effects of different pre- and post-nodes. Finally, we compare CICEB with traditional methods on two real-world datasets and show that it achieves superior effectiveness and robustness.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Becker, R., D’angelo, G., Ghobadi, S., Gilbert, H.: Fairness in influence maximization through randomization. J. Artif. Intell. Res. 73, 1251–1283 (2022)
Benson, A.R., Abebe, R., Schaub, M.T., Jadbabaie, A., Kleinberg, J.: Simplicial closure and higher-order link prediction. Proc. Natl. Acad. Sci. (2018)
Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 57–66 (2001)
Erkol, Ş, Mazzilli, D., Radicchi, F.: Effective submodularity of influence maximization on temporal networks. Phys. Rev. E 106(3), 034301 (2022)
He, Q., et al.: CAOM: a community-based approach to tackle opinion maximization for social networks. Inf. Sci. 513, 252–269 (2020)
Hernan, M.A., Robins, J.M.: Causal Inference: What If. Chapman Hill/CRC, Boca Raton (2020)
Huang, H., Shen, H., Meng, Z., Chang, H., He, H.: Community-based influence maximization for viral marketing. Appl. Intell. 49, 2137–2150 (2019)
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146 (2003)
Kilbertus, N., Rojas Carulla, M., Parascandolo, G., Hardt, M., Janzing, D., Schölkopf, B.: Avoiding discrimination through causal reasoning. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 420–429 (2007)
Li, S., Kong, F., Tang, K., Li, Q., Chen, W.: Online influence maximization under linear threshold model. In: Advances in Neural Information Processing Systems, vol. 33, pp. 1192–1204 (2020)
Ling, C., et al.: Deep graph representation learning and optimization for influence maximization. In: International Conference on Machine Learning, pp. 21350–21361. PMLR (2023)
Liu, B., Cong, G., Xu, D., Zeng, Y.: Time constrained influence maximization in social networks. In: 2012 IEEE 12th International Conference on Data Mining, pp. 439–448. IEEE (2012)
Ma, J., Wan, M., Yang, L., Li, J., Hecht, B., Teevan, J.: Learning causal effects on hypergraphs. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1202–1212 (2022)
Ma, L., et al.: Influence maximization in complex networks by using evolutionary deep reinforcement learning. IEEE Trans. Emerg. Top. Comput. Intell. (2022)
Panagopoulos, G., Malliaros, F.D., Vazirgianis, M.: Influence maximization using influence and susceptibility embeddings. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 511–521 (2020)
Pei, S., Makse, H.A.: Spreading dynamics in complex networks. J. Stat. Mech: Theory Exp. 2013(12), P12002 (2013)
Schölkopf, B., et al.: Toward causal representation learning. Proc. IEEE 109(5), 612–634 (2021)
Suo, Q., Guo, J.L., Shen, A.Z.: Information spreading dynamics in hypernetworks. Phys. A 495, 475–487 (2018)
Tang, S.: When social advertising meets viral marketing: sequencing social advertisements for influence maximization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Tang, Y., Shi, Y., Xiao, X.: Influence maximization in near-linear time: a martingale approach. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 1539–1554 (2015)
Zhu, J., Zhu, J., Ghosh, S., Wu, W., Yuan, J.: Social influence maximization in hypergraph in social networks. IEEE Trans. Netw. Sci. Eng. 6(4), 801–811 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Su, X., Qiu, J., Zhang, Z., Li, J. (2024). Causal-Inspired Influence Maximization in Hypergraphs Under Temporal Constraints. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1967. Springer, Singapore. https://doi.org/10.1007/978-981-99-8178-6_23
Download citation
DOI: https://doi.org/10.1007/978-981-99-8178-6_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8177-9
Online ISBN: 978-981-99-8178-6
eBook Packages: Computer ScienceComputer Science (R0)