基于因果干预缓解虚假关联的信息级联流行度预测

doi:10.11896/jsjkx.250400079

Abstract

Abstract: Information cascade popularity prediction is often affected by spurious correlations arising from internal cascade dynamics.Most existing methods assume that cascade data follows an independent and identically distributed pattern,which does not hold in real-world scenarios with complex diffusion processes.This mismatch leads to significant performance degradation on out-of-distribution data.To address this challenge and enhance model robustness and generalization under distributional shifts,this paper proposes CCP(Causal Cascade Prediction),a dual-intervention framework based on causal inference.Specifically,intra-cascade intervention randomly prunes nodes to break the misleading correlation between observed cascade size and final popularity,while inter-cascade intervention incorporates information from structurally similar cascades to introduce data diversity.CCP decouples popularity from non-causal factors such as structure and observation time,enabling the model to capture true causal drivers of information spread.Experimental results on the Weibo and APS datasets show that CCP outperforms the state-of-the-art CasCIFF method,achieving 2%~5% improvement in MSLE,and 2%~3% in MAPE,and demonstrates 5%~7% better generalization performance under the same baseline,validating its effectiveness in handling distributional shifts.

Key words: Information cascade, Causal intervention, Popularity prediction

CLC Number:

TP391

YU Liu, LI Shuo, KUANG Ping, ZHOU Fan, JIANG Tao. Causal Intervention-based Mitigation of Spurious Correlations in Information Cascade PopularityPrediction[J].Computer Science, 2026, 53(6): 304-314.

References

[1]ZHOU F,XU X,TRAJCEVSKI G,et al.A survey of informationcascade analysis:Models,predictions,and recent advances[J].ACM Computing Surveys,2021,54(2):1-36.
[2]SHEN H,WANG D,SONG C,et al.Modeling and predictingpopularity dynamics via reinforced poisson processes[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2014.
[3]IRIBARREN J L,MORO E.Affinity paths and information diffusion in social networks[J].Social Networks,2011,33(2):134-142.
[4]MA S,FENG L,LAI C H.Mechanistic modelling of viral spreading on empirical social network and popularity prediction[J].Scientific Reports,2018,8(1):13126.
[5]CHENG Z,ZHONG T,ZHANG S.Survey of RecommenderSystems Based on Graph Learning [J].Journal of Computer Science,2022;49(9):1-13.
[6]HE S,TIAN H,LYU X.Edge popularity prediction based on social-driven propagation dynamics[J].IEEE Communications Letters,2017,21(5):1027-1030.
[7]BHARGAVA S,GHOSH D S.Analysis of feature reductiontechniques for online news popularity prediction[J].Smart Moves Journal IJOSCIENCE,2018,4:11-17.
[8]MISHRA S,RIZOIU M A,XIE L.Feature driven and pointprocess approaches for popularity prediction[C]//Proceedings of the 25th ACM International on Conference on Information and Knowledge Management.2016:1069-1078.
[9]CHENG J,ADAMIC L,DOW P A,et al.Can cascades be predicted?[C]//Proceedings of the 23rd International Conference on World Wide Web.2014:925-936.
[10]DALEY D J,VERE-JONES D.An introduction to the theory of point processes:volume I:elementary theory and methods[M].Springer Science & Business Media,2006.
[11]HAWKES A G.Spectra of some self-exciting and mutually exciting point processes[J].Biometrika,1971,58(1):83-90.
[12]YU L,XU X,TRAJCEVSKI G,et al.Transformer-enhancedHawkes process with decoupling training for information cascade prediction[J].Knowledge-Based Systems,2022,255:109740.
[13]YU L,XU X,ZHONG T,et al.Linking transformer to hawkes process for information cascade prediction(student abstract)[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2022:13103-13104.
[14]ZHAO Q,ERDOGDU M A,HE H Y,et al.Seismic:A self-exciting point process model for predicting tweet popularity[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2015:1513-1522.
[15]DERAKHSHANI R,ESMAEELI H,AMIRI A.Phase II Monitoring of Poisson Regression Profiles in Multi-Stage Processes[J].International Journal of Reliability,Quality and Safety Engineering,2020,27(4):2050012.
[16]ZHAO Q,ZHANG Y,FENG X.Predicting information diffusion via deep temporalconvolutional networks[J].Information Systems,2022,108:102045.
[17]WANG J,ZHENG V W,LIU Z,et al.Topological recurrentneural network for diffusion prediction[C]//2017 IEEE International Conference on Data Mining(ICDM).IEEE,2017:475-484.
[18]GRAVES A.Long short-term memory[M]//Supervised Sequence Labelling with Recurrent Neural Networks.Berlin:Springer,2012:37-45.
[19]JAIN L,KATARYA R,SACHDEVA S.Opinion leaders for information diffusion using graph neural network in online social networks[J].ACM Transactions on the Web,2023,17(2):1-37.
[20]CAO Q,SHEN H,GAO J,et al.Popularity prediction on social platforms with coupled graph neural networks[C]//Proceedings of the 13th International Conference on Web Search and Data Mining.2020:70-78.
[21]CUI P,WANG J.Out-of-distribution(OOD) detection based on deep learning:A review[J].Electronics,2022,11(21):3500.
[22]ZHOU F,YU L,XU X,et al.Decoupling representation and regressor for long-tailed information cascade prediction[C]//Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval.2021:1875-1879.
[23]ZHOU F,XU X,ZHANG K,et al.Variational information diffusion for probabilistic cascades prediction[C]//IEEE INFOCOM 2020-IEEE Conference on Computer Communications.IEEE,2020:1618-1627.
[24]SUH B,HONG L,PIROLLI P,et al.Want to be retweeted? large scale analytics on factors impacting retweet in twitter network[C]//2010 IEEE Second International Conference on Social Computing.IEEE,2010:177-184.
[25]YU H,XIE L,SANNER S.The lifecyle of a youtube video:Phases,content and popularity[C]//Proceedings of the Internatio-nal AAAI Conference on Web and Social Media.2015:533-542.
[26]CARTA S,PODDA A S RECUPERO D R,et al.Popularity prediction of instagram posts[J].Information,2020,11(9):453.
[27]RIZOIU M A,XIE L,SANNER S,et al.Expecting to be hip:Hawkes intensity processes for social media popularity[C]//Proceedings of the 26th International Conference on World Wide Web.2017:735-744.
[28]GAO J,SHEN H,LIU S,et al.Modeling and predicting retweeting dynamics via a mixture process[C]//Proceedings of the 25th InternationalConference Companion on World Wide Web.2016:33-34.
[29]SHANG J,HUANG S,ZHANG D,et al.RNe2Vec:information diffusion popularity prediction based on repost network embedding[J].Computing,2021,103:271-289.
[30]TANG X,LIAO D,HUANG W,et al.Fully exploiting cascade graphs for real-time forwarding prediction[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2021:582-590.
[31]CHEN X,ZHOU F,ZHANG K,et al.Information diffusion prediction via recurrent cascades convolution[C]//2019 IEEE 35th International Conference on Data Engineering(ICDE).IEEE,2019:770-781.
[32]WANG Y,WANG X,JIA T.Ccasgnn:Collaborative cascadeprediction based on graph neural networks[C]//2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design(CSCWD).IEEE,2022:810-815.
[33]DENG Z,ZHENG X,TIAN H,et al.Deep causal learning:representation,discovery and inference[J].arXiv:2211.03374,2022.
[34]BAIARDI A,NAGHI A A.The value added of machine learning to causal inference:Evidence from revisited studies[J].The Econometrics Journal,2024,27(2):213-234.
[35]PEARL J.Models,reasoning and inference[M].Cambridge,UK:Cambridge University Press,2000:3.
[36]TRAN T Q,FUKUCHI K,AKIMOTO Y,et al.Unsupervisedcausal binary concepts discovery with vae for black-box model explanation[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2022:9614-9622.
[37]FEDER A,KEITH K A,MANZOOR E,et al.Causal inference in natural language processing:Estimation,prediction,interpretation and beyond[J].Transactions of the Association for Computational Linguistics,2022,10:1138-1158.
[38]WANG T,HUANG J,ZHANG H,et al.Visual commonsense r-cnn[C]//Proceedings of the IEEE/CVF Conference on Compu-ter Vision and Pattern Recognition.2020:10760-10770.
[39]ZHANG D,ZHANG H,TANG J,et al.Causal intervention for weakly-supervised semantic segmentation[J].Advances in Neural Information Processing Systems,2020,33:655-666.
[40]WANG W,LIN X,FENG F,et al.Causal representation lear-ning for out-of-distribution recommendation[C]//Proceedings of the ACM Web Conference 2022.2022:3562-3571.
[41]PEROZZI B,AL-RFOU R,SKIENA S.Deepwalk:Online lear-ning of social representations[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining.2014:701-710.
[42]GROVER A,LESKOVEC J.node2vec:Scalable feature learning for networks[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mi-ning.2016:855-864.
[43]VELIČKOVIĆ P,CUCURULL G,CASANOVA A,et al.Graph attention networks[J].arXiv:1710.10903,2017.
[44]SANDRYHAILA A,MOURA J M F.Discrete signal processing on graphs:Graph fourier transform[C]//2013 IEEE International Conference on Acoustics,Speech and Signal Processing.IEEE,2013:6167-6170.
[45]SHUMAN D I,NARANG S K,FROSSARD P,et al.The emerging field of signal processing on graphs:Extending high-dimensional data analysis to networks and other irregular domains[J].IEEE Signal Processing Magazine,2013,30(3):83-98.
[46]ZHU H,YUAN S,LIU X,et al.CasCIFF:A cross-domain information fusion framework tailored for cascade prediction in social networks[J].Knowledge-Based Systems,2024,303:112391.
[47]REICHENBACH H.The direction of time[M].University of California Press,1991.
[48]PAWLOWSKI N,COELHO DE CASTRO D,GLOCKER B.Deep structural causal models for tractable counterfactual inference[J].Advances in Neural Information Processing Systems,2020,33:857-869.
[49]HAGMAYER Y,SLOMAN S A,LAGNADO D A,et al.Causal reasoning through intervention[M]//Causal Learning:Psycho-logy,Philosophy,and Computation.2007:86-100.
[50]WEI Y,WANG X,NIE L,et al.Causal inference for knowledge graph based recommendation[J].IEEE Transactions on Know-ledge and Data Engineering,2022,35(11):11153-11164.
[51]NG I,ZHU S,FANG Z,et al.Masked gradient-based causalstructure learning[C]//Proceedings of the 2022 SIAM International Conference on Data Mining(SDM).Society for Industrial and Applied Mathematics,2022:424-432.
[52]CAO Q,SHEN H,CEN K,et al.Deephawkes:Bridging the gap between prediction and understanding of information cascades [C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management.2017:1149-1158.

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Causal Intervention-based Mitigation of Spurious Correlations in Information Cascade PopularityPrediction

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