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Mining hidden non-redundant causal relationships in online social networks

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Abstract

Causal discovery is crucial to obtain a deep understanding of the actual mechanism behind the online social network, e.g., identifying the influential individuals and understanding the interaction among user behavior sequences. However, detecting causal directions and pruning causal redundancy of online social networks are still the great challenge of existing research. This paper proposed a constraint-based approach, minimal causal network (MCN), to mine hidden non-redundant causal relationships behind user behavior sequences. Under the MCN, the transfer entropy with the adaptive causal time lag is used to detect causal directions and find causal time lags, while a permutation-based significance test is proposed to prune redundant edges. Experiments on simulated data verify the effectiveness of our proposed method. We also apply our approach to real-world data from Sina Weibo and reveal some interesting discoveries.

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Notes

  1. “1192329374” is one of China’s most famous hosts of a television show, called Na Xie.

  2. Nike: https://en.wikipedia.org/wiki/Nike,_Inc.

  3. Huawei: https://en.wikipedia.org/wiki/Huawei.

References

  1. Aiello LM, Barrat A, Schifanella R, Cattuto C, Markines B, Menczer F (2012) Friendship prediction and homophily in social media. ACM Trans Web (TWEB) 6(2):9

    Google Scholar 

  2. Althoff T, Jindal P, Leskovec J (2017) Online actions with offline impact: how online social networks influence online and offline user behavior. In: Proceedings of the tenth ACM international conference on web search and data mining, pp 537–546. ACM

  3. Aral S, Nicolaides C (2017) Exercise contagion in a global social network. Nat Commun 8:14753

    Article  Google Scholar 

  4. Bakshy E, Eckles D, Yan R, Rosenn I (2012) Social influence in social advertising: evidence from field experiments. In: Proceedings of the 13th ACM conference on electronic commerce, pp 146–161. ACM

  5. Bakshy E, Hofman JM, Mason WA, Watts DJ (2011) Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of the fourth ACM international conference on Web search and data mining, pp 65–74

  6. Bakshy E, Rosenn I, Marlow C, Adamic L (2012) The role of social networks in information diffusion. In: Proceedings of the 21st international conference on world wide web, pp 519–528. ACM

  7. Barnett L, Barrett AB, Seth AK (2009) Granger causality and transfer entropy are equivalent for Gaussian variables. Phys Rev Lett 103(23):238701

    Article  Google Scholar 

  8. Bauer M, Cox JW, Caveness MH, Downs JJ, Thornhill NF (2007) Finding the direction of disturbance propagation in a chemical process using transfer entropy. IEEE Trans Control Syst Technol 15(1):12–21

    Article  Google Scholar 

  9. Bonacich P (1972) Factoring and weighting approaches to status scores and clique identification. J Math Sociol 2(1):113–120

    Article  Google Scholar 

  10. Cai R, Zhang Z, Hao Z (2013) SADA: a general framework to support robust causation discovery. In: ICML pp 208–216

  11. Cai R, Zhang Z, Hao Z, Winslett M (2017) Understanding social causalities behind human action sequences. IEEE Trans Neural Netw Learn Syst 28(8):1801–1813

    Article  MathSciNet  Google Scholar 

  12. Cha M, Haddadi H, Benevenuto F, Gummadi PK (2010) Measuring user influence in twitter: the million follower fallacy. ICWSM 10(10–17):30

    Google Scholar 

  13. Chikhaoui B, Chiazzaro M, Wang S (2015) A new granger causal model for influence evolution in dynamic social networks: the case of DBLP. In: AAAI, pp 51–57

  14. Duan P, Yang F, Chen T, Shah SL (2013) Direct causality detection via the transfer entropy approach. IEEE Trans Control Syst Technol 21(6):2052–2066

    Article  Google Scholar 

  15. Freeman LC (1978) Centrality in social networks conceptual clarification. Soc Netw 1(3):215–239

    Article  Google Scholar 

  16. Gollob HF, Reichardt CS (1987) Taking account of time lags in causal models. Child Dev 1:80–92

    Article  Google Scholar 

  17. Granger Clive WJ (1969) Investigating causal relations by econometric models and cross-spectral methods. Econ J Econ Soc 1:424–438

    MATH  Google Scholar 

  18. Granger CWJ (1980) Testing for causality: a personal viewpoint. J Econ Dyn Control 2:329–352

    Article  MathSciNet  Google Scholar 

  19. Hoyer PO, Janzing D, Mooij JM, Peters J, Schölkopf B (2009) Nonlinear causal discovery with additive noise models. In: Advances in neural information processing systems, pp 689–696

  20. Jin L, Chen Y, Wang T, Hui P, Vasilakos AV (2013) Understanding user behavior in online social networks: a survey. IEEE Commun Mag 51(9):144–150

    Article  Google Scholar 

  21. Liu S, Zheng H, Shen H, Cheng X, Liao X (2017) Learning concise representations of users’ influences through online behaviors. In: Proceedings of the 26th international joint conference on artificial intelligence, pp 2351–2357. AAAI Press

  22. Marinazzo D, Liao W, Chen H, Stramaglia S (2011) Nonlinear connectivity by granger causality. Neuroimage 58(2):330–338

    Article  Google Scholar 

  23. Overbey LA, Todd MD (2009) Dynamic system change detection using a modification of the transfer entropy. J Sound Vib 322(1):438–453

    Article  Google Scholar 

  24. Page L, Brin S, Motwani R, Winograd T (1999) The pagerank citation ranking: bringing order to the web. Technical report, Stanford InfoLab

  25. Pearl J (2009) Causality. Cambridge University Press, Cambridge

    Book  Google Scholar 

  26. Peters J, Janzing D, Scholkopf B (2011) Causal inference on discrete data using additive noise models. IEEE Trans Pattern Anal Mach Intell 33(12):2436–2450

    Article  Google Scholar 

  27. Richardson M, Domingos P (2002) Mining knowledge-sharing sites for viral marketing. In: Proceedings of ACM SIGKDD, pp 61–70. ACM

  28. Rissanen J (1978) Modeling by shortest data description. Automatica 14(5):465–471

    Article  Google Scholar 

  29. Shimizu S, Hoyer PO, Hyvärinen A, Kerminen A (2006) A linear non-Gaussian acyclic model for causal discovery. J Mach Learn Res 7:2003–2030

    MathSciNet  MATH  Google Scholar 

  30. Spirtes P, Glymour CN, Scheines R (2000) Causation, prediction, and search. MIT Press, London

    MATH  Google Scholar 

  31. Sun J, Bollt EM (2014) Causation entropy identifies indirect influences, dominance of neighbors and anticipatory couplings. Physica D: Nonlinear Phenom 267:49–57

    Article  MathSciNet  Google Scholar 

  32. Sun J, Taylor D, Bollt EM (2015) Causal network inference by optimal causation entropy. SIAM J Appl Dyn Syst 14(1):73–106

    Article  MathSciNet  Google Scholar 

  33. Teutle ARM (2010) Twitter: network properties analysis. In: 2010 20th international conference on electronics, communications and computer (CONIELECOMP), pp 180–186. IEEE

  34. Tunkelang D (2009) A twitter analog to pagerank. The Noisy Channel. https://thenoisychannel.com/2009/01/13/a-twitter-analog-to-pagerank

  35. Ver Steeg G, Galstyan A (2012) Information transfer in social media. In: Proceedings of the 21st international conference on world wide web, pp 509–518. ACM

  36. Vicente R, Wibral M, Lindner M, Pipa G (2011) Transfer entropy—a model-free measure of effective connectivity for the neurosciences. J Comput Neurosci 30(1):45–67

    Article  MathSciNet  Google Scholar 

  37. Walker SK (2011) Connected: the surprising power of our social networks and how they shape our lives. J Family Theory Rev 3(3):220–224

    Article  Google Scholar 

  38. Wang B, Chen G, Luoyi F, Song L, Wang X (2017) Drimux: dynamic rumor influence minimization with user experience in social networks. IEEE Trans Knowl Data Eng 10:2168–2181

    Article  Google Scholar 

  39. Weng J, Lim E-P, Jiang J, He Q (2010) Twitterrank: finding topic-sensitive influential twitterers. In: Proceedings of the third ACM international conference on web search and data mining, pp 261–270. ACM

  40. Zhang K, Hyvärinen A (2009) On the identifiability of the post-nonlinear causal model. In: Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence, pp 647–655. AUAI Press

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Acknowledgements

This work is financially supported by NSFC-Guangdong Joint Found (U1501254), Natural Science Foundation of China (61472089), Natural Science Foundation of Guangdong (2014A030306004, 2014A030308008), Science and Technology Planning Project of Guangdong (2015B010108006, 2015B010131015, 2015B010129014), Guangdong High-level personnel of special support program (2015TQ01X140), Pearl River S&T Nova Program of Guangzhou (201610010101), and Science and Technology Planning Project of Guangzhou (201604016075).

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Authors and Affiliations

  1. School of Computer Science, Guangdong University of Technology, Guangzhou, China

    Wei Chen, Ruichu Cai, Zhifeng Hao, Chang Yuan & Feng Xie

  2. School of Mathematics and Big Data, Foshan University, Foshan, China

    Zhifeng Hao

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  1. Wei Chen
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  2. Ruichu Cai
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  3. Zhifeng Hao
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  4. Chang Yuan
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  5. Feng Xie
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Correspondence to Ruichu Cai.

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Chen, W., Cai, R., Hao, Z. et al. Mining hidden non-redundant causal relationships in online social networks. Neural Comput & Applic 32, 6913–6923 (2020). https://doi.org/10.1007/s00521-019-04161-5

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