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Data Mining and Knowledge Discovery

, Volume 33, Issue 2, pp 499–525 | Cite as

Model-free inference of diffusion networks using RKHS embeddings

  • Shoubo Hu
  • Bogdan CautisEmail author
  • Zhitang Chen
  • Laiwan Chan
  • Yanhui Geng
  • Xiuqiang He
Article
  • 311 Downloads
Part of the following topical collections:
  1. Journal Track of ECML PKDD 2019

Abstract

We revisit in this paper the problem of inferring a diffusion network from information cascades. In our study, we make no assumptions on the underlying diffusion model, in this way obtaining a generic method with broader practical applicability. Our approach exploits the pairwise adoption-time intervals from cascades. Starting from the observation that different kinds of information spread differently, these time intervals are interpreted as samples drawn from unknown (conditional) distributions. In order to statistically distinguish them, we propose a novel method using Reproducing Kernel Hilbert Space embeddings. Experiments on both synthetic and real-world data from Twitter and Flixster show that our method significantly outperforms the state-of-the-art methods. We argue that our algorithm can be implemented by parallel batch processing, in this way meeting the needs in terms of efficiency and scalability of real-world applications.

Keywords

Diffusion networks Edge inference Clustering Kernel embeddings 

Notes

References

  1. Arthur D, Vassilvitskii S (2007) K-means++: The advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on discrete algorithms, SODA ’07, pp 1027–1035, Philadelphia. Society for Industrial and Applied MathematicsGoogle Scholar
  2. Chen W, Wang Y, Yuan Y, Wang Q (2016) Combinatorial multi-armed bandit and its extension to probabilistically triggered arms. J Mach Learn Res 17(50):1–33MathSciNetzbMATHGoogle Scholar
  3. Chen Z, Zhang K, Chan L, Schlkopf B (2014) Causal discovery via reproducing kernel hilbert space embeddings. Neural Comput 26(7):1484–1517 PMID: 24708374MathSciNetCrossRefGoogle Scholar
  4. Dhillon IS, Guan Y, Kulis B (2004) Kernel k-means: spectral clustering and normalized cuts. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’04, pp 551–556. ACM, New YorkGoogle Scholar
  5. Du N, Song L, Woo H, Zha H (2013) Uncover topic-sensitive information diffusion networks. In: Carvalho CM, Ravikumar P (eds), Proceedings of the sixteenth international conference on artificial intelligence and statistics, vol 31 of proceedings of machine learning research, pp 229–237. PMLR, Scottsdale, ArizonaGoogle Scholar
  6. Du N, Song L, Yuan M, Smola AJ (2012) Learning networks of heterogeneous influence. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems 25. Curran Associates Inc, pp 2780–2788Google Scholar
  7. Easley D, Kleinberg J (2010) Networks, crowds, and markets: reasoning about a highly connected world. Cambridge University Press, New YorkCrossRefzbMATHGoogle Scholar
  8. Fraley RC, Raftery AE (2002) Model-based clustering, discriminant analysis, and density estimation. J Am Stat Assoc 97:611–631MathSciNetCrossRefzbMATHGoogle Scholar
  9. Gomez-Rodriguez M, Balduzzi D, Schölkopf B (2011) Uncovering the temporal dynamics of diffusion networks. In: Proceedings of the 28th international conference on international conference on machine learning, ICML’11, pp 561–568. Omnipress, USAGoogle Scholar
  10. Gomez-Rodriguez M, Leskovec J, Krause A (2012) Inferring networks of diffusion and influence. ACM Trans Knowl Discov Data 5(4):21:1–21:37CrossRefGoogle Scholar
  11. Gomez-Rodriguez M, Leskovec J, Schölkopf B (2013) Structure and dynamics of information pathways in online media. In: Proceedings of the sixth ACM international conference on web search and data mining, WSDM ’13, pp 23–32. ACM, New YorkGoogle Scholar
  12. Gomez-Rodriguez M, Schölkopf B (2012) Influence maximization in continuous time diffusion networks. In: Proceedings of the 29th international conference on international conference on machine learning, ICML’12, pp 579–586. Omnipress, USAGoogle Scholar
  13. Gomez-Rodriguez M, Song L, Du N, Zha H, Schölkopf B (2016) Influence estimation and maximization in continuous-time diffusion networks. ACM Trans Inf Syst 34(2):9:1–9:33CrossRefGoogle Scholar
  14. Goyal A, Bonchi F, Lakshmanan LV (2010) Learning influence probabilities in social networks. In: Proceedings of the third ACM international conference on web search and data mining, WSDM ’10, pp 241–250, ACM, New YorkGoogle Scholar
  15. Grabowicz PA, Ganguly N, Gummadi KP (2016) Distinguishing between topical and non-topical information diffusion mechanisms in social media. In: Proceedings of the 10th international conference on web and social media, pp 151–160Google Scholar
  16. Gretton A, Borgwardt KM, Rasch MJ, Schölkopf B, Smola A (2012) A kernel two-sample test. J Mach Learn Res 13(1):723–773MathSciNetzbMATHGoogle Scholar
  17. Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the fourth ACM conference on recommender systems, RecSys ’10, pp 135–142. ACM, New YorkGoogle Scholar
  18. Jegelka S, Gretton A, Schölkopf B, Sriperumbudur BK, von Luxburg U (2009) Generalized clustering via kernel embeddings. In: Mertsching B, Hund M, Aziz Z (eds) KI 2009: advances in artificial intelligence. Springer, Berlin, pp 144–152CrossRefGoogle Scholar
  19. Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’03, pp 137–146. ACM, New YorkGoogle Scholar
  20. Lei S, Maniu S, Mo L, Cheng R, Senellart P (2015) Online influence maximization. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’15, pp 645–654. ACM, New YorkGoogle Scholar
  21. McLachlan G, Peel D (2004) Finite mixture models. Wiley series in probability and statistics: applied probability and statistics. Wiley, LondonGoogle Scholar
  22. Muandet K, Fukumizu K, Sriperumbudur B, Schölkopf B (2017) Kernel mean embedding of distributions: a review and beyond. Foundations Trends Mach Learn 10(1–2):1–141CrossRefzbMATHGoogle Scholar
  23. Myers S, Leskovec J (2010) On the convexity of latent social network inference. In: Advances in neural information processing systems 23, pp 741–1749. Curran Associates, IncGoogle Scholar
  24. Rahimi A, Recht B (2008) Random features for large-scale kernel machines. In: Advances in neural information processing systems 20, pp 1177–1184. Curran Associates, IncGoogle Scholar
  25. Romero DM, Meeder B, Kleinberg J (2011) Differences in the mechanics of information diffusion across topics: Idioms, political hashtags, and complex contagion on twitter. In: Proceedings of the 20th international conference on world wide web, WWW ’11, pp 695–704. ACM, New YorkGoogle Scholar
  26. Rong Y, Zhu Q, Cheng H (2016) A model-free approach to infer the diffusion network from event cascade. In: Proceedings of the 25th ACM international on conference on information and knowledge management, CIKM ’16, pp 1653–1662. ACM, New YorkGoogle Scholar
  27. Rudin W (2017) Fourier analysis on groups. Dover books on mathematics. Dover Publications, NYGoogle Scholar
  28. Saito K, Nakano R, Kimura M (2008) Prediction of information diffusion probabilities for independent cascade model. In: Proceedings of the 12th international conference on knowledge-based intelligent information and engineering systems, Part III, KES ’08, pp 67–75. Springer, BerlinGoogle Scholar
  29. Schölkopf B, Smola AJ (2001) Learning with Kernels: support vector machines, regularization, optimization, and beyond. MIT Press, CambridgeGoogle Scholar
  30. Shirazi S, Harandi MT, Sanderson C, Alavi A, Lovell BC (2012) Clustering on grassmann manifolds via kernel embedding with application to action analysis. In: 2012 19th IEEE international conference on image processing, pp 781–784Google Scholar
  31. Smola A, Gretton A, Song L, Schölkopf B (2007) A hilbert space embedding for distributions. In: Hutter M, Servedio RA, Takimoto E (eds) Algorithmic learning theory. Springer, Berlin, pp 13–31CrossRefGoogle Scholar
  32. Song L, Fukumizu K, Gretton A (2013) Kernel embeddings of conditional distributions: a unified kernel framework for nonparametric inference in graphical models. IEEE Signal Process Mag 30(4):98–111CrossRefGoogle Scholar
  33. Song L, Huang J, Smola A, Fukumizu K (2009) Hilbert space embeddings of conditional distributions with applications to dynamical systems. In: Proceedings of the 26th annual international conference on machine learning, ICML ’09, pp 961–968. ACM, New YorkGoogle Scholar
  34. Vaswani S, Lakshmanan V, Schmidt M (2015) Influence maximization with bandits. In: NIPS workshop on networks in the social and information sciencesGoogle Scholar
  35. Villmann T, Biehl M, Hammer B, Verleysen M (2009) Similarity-based clustering: recent developments and biomedical applications, vol 5400. Springer, BerlinCrossRefGoogle Scholar
  36. Wang H, Shi X, Yeung D-Y (2017) Relational deep learning: a deep latent variable model for link prediction. In: AAAI conference on artificial intelligence, pp 2688–2694Google Scholar
  37. Watts D (2004) Six degrees: the science of a connected age. W. W. NortonGoogle Scholar
  38. Watts DJ, Dodds PS (2007) Influentials, networks, and public opinion formation. J Consum Res 34(4):441–458CrossRefGoogle Scholar
  39. Wen Z, Kveton B, Valko M, Vaswani S (2017) Online influence maximization under independent cascade model with semi-bandit feedback. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in neural information processing systems 30. Curran Associates Inc, pp 3022–3032Google Scholar

Copyright information

© The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.The Chinese University of Hong KongShatinHong Kong
  2. 2.University of Paris-SudOrsayFrance
  3. 3.Huawei Noah’s Ark LabShatinHong Kong

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