Abstract
Independent cascade (IC) model is a widely used influence propagation model for social networks. In this paper, we incorporate the concept and techniques from causal inference to study the identifiability of parameters from observational data in extended IC model with unobserved confounding factors, which models more realistic propagation scenarios but is rarely studied in influence propagation modeling before. We provide the conditions for the identifiability or unidentifiability of parameters for several special structures including the Markovian IC model, semi-Markovian IC model, and IC model with a global unobserved variable. Parameter identifiability is important for other tasks such as influence maximization under the diffusion networks with unobserved confounding factors.
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References
Abrahao, B., Chierichetti, F., Kleinberg, R., Panconesi, A.: Trace complexity of network inference. arXiv e-prints, pp. arXiv-1308 (2013)
Chen, W., Lakshmanan, L.V., Castillo, C.: Information and Influence Propagation in Social Networks. Morgan & Claypool Publishers (2013)
Daneshmand, H., Gomez-Rodriguez, M., Song, L., Schölkopf, B.: Estimating diffusion network structures: recovery conditions, sample complexity & soft-thresholding algorithm. In: ICML (2014)
Drton, M., Foygel, R., Sullivant, S.: Global identifiability of linear structural equation models. Ann. Stat. 39(2), 865–886 (2011)
Du, N., Liang, Y., Balcan, M., Song, L.: Influence function learning in information diffusion networks. In: ICML 2014, Beijing, China, 21–26 June 2014 (2014)
Du, N., Song, L., Gomez-Rodriguez, M., Zha, H.: Scalable influence estimation in continuous-time diffusion networks. In: NIPS 2013, Lake Tahoe, Nevada, United States, 5–8 December 2013, pp. 3147–3155 (2013)
Du, N., Song, L., Smola, A.J., Yuan, M.: Learning networks of heterogeneous influence. In: NIPS 2012, Lake Tahoe, Nevada, United States, 3–6 December 2012, pp. 2789–2797 (2012)
Feng, S., Chen, W.: Causal inference for influence propagation - identifiability of the independent cascade model. CoRR abs/2107.04224 (2021). https://arxiv.org/abs/2107.04224
Foygel, R., Draisma, J., Drton, M.: Half-trek criterion for generic identifiability of linear structural equation models. Ann. Stat. 1682–1713 (2012)
Gomez-Rodriguez, M., Balduzzi, D., Schölkopf, B.: Uncovering the temporal dynamics of diffusion networks. arXiv preprint arXiv:1105.0697 (2011)
Gomez-Rodriguez, M., Leskovec, J., Krause, A.: Inferring networks of diffusion and influence. In: KDD 2010 (2010)
Goyal, A., Bonchi, F., Lakshmanan, L.V., Venkatasubramanian, S.: On minimizing budget and time in influence propagation over social networks. Soc. Netw. Anal. Min. 3(2), 179–192 (2013)
He, X., Xu, K., Kempe, D., Liu, Y.: Learning influence functions from incomplete observations. arXiv e-prints, pp. arXiv-1611 (2016)
Huang, Y., Valtorta, M.: Identifiability in causal Bayesian networks: a sound and complete algorithm. In: AAAI, pp. 1149–1154 (2006)
Huang, Y., Valtorta, M.: Pearl’s calculus of intervention is complete. arXiv preprint arXiv:1206.6831 (2012)
Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. In: KDD (2003)
Myers, S.A., Leskovec, J.: On the convexity of latent social network inference. arXiv e-prints, pp. arXiv-1010 (2010)
Narasimhan, H., Parkes, D.C., Singer, Y.: Learnability of influence in networks. In: Proceedings of the 29th Annual Conference on Neural Information Processing Systems (2015)
Netrapalli, P., Sanghavi, S.: Finding the graph of epidemic cascades. arXiv preprint arXiv:1202.1779 (2012)
Pearl, J.: Causality, 2nd edn. Cambridge University Press, Cambridge (2009)
Pouget-Abadie, J., Horel, T.: Inferring graphs from cascades: a sparse recovery framework. arXiv e-prints, pp. arXiv-1505 (2015)
Shpitser, I., Pearl, J.: Identification of joint interventional distributions in recursive semi-Markovian causal models. In: Proceedings of the 21st National Conference on Artificial Intelligence, pp. 1219–1226 (2006)
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)
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Feng, S., Chen, W. (2021). Causal Inference for Influence Propagation—Identifiability of the Independent Cascade Model. In: Mohaisen, D., Jin, R. (eds) Computational Data and Social Networks. CSoNet 2021. Lecture Notes in Computer Science(), vol 13116. Springer, Cham. https://doi.org/10.1007/978-3-030-91434-9_2
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DOI: https://doi.org/10.1007/978-3-030-91434-9_2
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