[1] ELKAHKY A, SONG Y, HE X D. A Multi-view Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems // Proc of the 24th International Conference on World Wide Web. New York, USA: ACM, 2015: 278-288.
[2] WANG H W, WANG R J, WEN C, et al. Author Name Disambiguation on Heterogeneous Information Network with Adversarial Representation Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(1): 238-245.
[3] ZHANG Y T, WU Y Q, LE R, et al. Modeling Dual Period-Va-rying Preferences for Takeaway Recommendation // Proc of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mi-ning. New York, USA: ACM, 2023: 5628-5638.
[4] QI T, WU F Z, WU C H, et al. HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation // Proc of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Proce-ssing(Long Papers). Stroudsburg, USA: ACL, 2021: 5446-5456.
[5] SUN L, RAO Y,LAN Y Q, et al. HG-SL: Jointly Learning of Glo-bal and Local User Spreading Behavior for Fake News Early Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 2023, 37(4): 5248-5256.
[6] MIN E X, RONG Y, BIAN Y T, et al. Divide-and-Conquer: Post-User Interaction Network for Fake News Detection on Social Media // Proc of the ACM Web Conference. New York, USA: ACM, 2022: 1148-1158.
[7] TAO X, WANG L, LIU Q, et al. Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detection // Proc of the ACM Web Conference. New York, USA: ACM, 2024: 4150-4159.
[8] CHEN L, LI G Y, WEI Z Y, et al. A Progressive Framework for Role-Aware Rumor Resolution // Proc of the 29th International Conference on Computational Linguistics. Stroudsburg, USA: ACL, 2022: 2748-2758.
[9] 张雪芹,刘 岗,王智能,等.基于多特征融合和深度学习的微观扩散预测.清华大学学报(自然科学版), 2024, 64(4): 688-699.
(ZHANG X Q, LIU G, WANG Z N. Microscopic Diffusion Prediction Based on Multifeature Fusion and Deep Learning. Journal of Tsinghua University(Science and Technology), 2024, 64(4): 688-699.)
[10] HODAS N O, LERMAN K. The Simple Rules of Social Contagion. Scientific Reports, 2014, 4(1). DOI: 10.1038/srep04343.
[11] GOMEZ-RODRIGUEZ M, LESKOVEC J, BALDUZZI D, et al. Un-covering the Structure and Temporal Dynamics of Information Pro-pagation. Network Science, 2014, 2(1). DOI: 10.1017/nws.2014.3.
[12] CHENG J, ADAMIC L, DOW P A, et al. Can Cascades Be Predicted? // Proc of the 23rd International Conference on World Wide Web. New York, USA: ACM, 2014: 925-936.
[13] GAO S, MA J, CHEN Z M. Effective and Effortless Features for Popularity Prediction in Microblogging Network // Proc of the 23rd International Conference on World Wide Web. New York, USA: ACM, 2014: 269-270.
[14] ZHANG J, TANG J, ZHONG Y Y, et al. StructInf: Mining Structural Influence from Social Streams. Proceedings of the AAAI Conference on Artificial Intelligence, 2017, 31(1): 73-79.
[15] YANG C, SUN M S, LIU H R, et al. Neural Diffusion Model for Microscopic Cascade Study. IEEE Transactions on Knowledge and Data Engineering, 2021, 33(3): 1128-1139.
[16] WANG J, ZHENG V W, LIU Z M, et al. Topological Recurrent Neural Network for Diffusion Prediction // Proc of the IEEE International Conference on Data Mining. Washington, USA: IEEE, 2017: 475-484.
[17] ISLAM M R, MUTHIAH S, ADHIKARI B, et al. DeepDiffuse: Predicting the "Who" and "When" in Cascades // Proc of the IEEE International Conference on Data Mining. Washington, USA: IEEE, 2018: 1055-1060.
[18] WANG Z T, CHEN C Y, LI W J. A Sequential Neural Information Diffusion Model with Structure Attention // Proc of the 27th ACM International Conference on Information and Knowledge Management. New York, USA: ACM, 2018: 1795-1798.
[19] LI H C, XIA C H, WANG T B, et al. GRASS: Learning Spatial-Temporal Properties from Chainlike Cascade Data for Microscopic Diffusion Prediction. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(11): 16313-16327.
[20] WANG D, WEI L W, YUAN C Y, et al. Cascade-Enhanced Graph Convolutional Network for Information Diffusion Prediction // Proc of the 27th International Conference on Database Systems for Advanced Applications. Berlin, Germany: Springer, 2022: 615-631.
[21] WANG R J, HUANG Z J, LIU S Z, et al. DyDiff-VAE: A Dynamic Variational Framework for Information Diffusion Prediction // Proc of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2021: 163-172.
[22] YUAN C Y, LI J C, ZHOU W, et al. DyHGCN: A Dynamic He-terogeneous Graph Convolutional Network to Learn Users′ Dynamic Preferences for Information Diffusion Prediction // Proc of the European Conference on Machine Learning and Knowledge Discovery in Databases. Berlin, Germany: Springer, 2021: 347-363.
[23] SUN L, RAO Y, ZHANG X B, et al. MS-HGAT: Memory-Enhanced Sequential Hypergraph Attention Network for Information Diffusion Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 2022, 36(4): 4156-4164.
[24] YANG C, WANG H, TANG J, et al. Full-Scale Information Diffusion Prediction with Reinforced Recurrent Networks. IEEE Tran-sactions on Neural Networks and Learning Systems, 2023, 34(5): 2271-2283.
[25] JIAO P F, CHEN H Q, BAO Q, et al. Enhancing Multi-scale Di-ffusion Prediction via Sequential Hypergraphs and Adversarial Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 2024, 38(8): 8571-8581.
[26] QIAO H L, FENG S S, LI X T, et al. RotDiff: A Hyperbolic Rotation Representation Model for Information Diffusion Prediction // Proc of the 32nd ACM International Conference on Information and Knowledge Management. New York, USA: ACM, 2023: 2065-2074.
[27] MINAMI K, NAKAJIMA H, TOYOSHIMA T. Real-Time Discrimination of Ventricular Tachyarrhythmia with Fourier-Transform Neural Network. IEEE Transactions on Biomedical Engineering, 1999, 46(2): 179-185.
[28] GOTHWAL H, KEDAWAT S, KUMAR R. Cardiac Arrhythmias De-tection in an ECG Beat Signal Using Fast Fourier Transform and Artificial Neural Network. Journal of Biomedical Science and Engineering, 2011, 4(4). DOI: 10.4236/jbise.2011.44039.
[29] MIRONOVOVA M, BÍLA J. Fast Fourier Transform for Feature Extraction and Neural Network for Classification of Electrocardiogram Signals // Proc of the 4th International Conference on Future Generation Communication Technology. Washington, USA: IEEE, 2015: 112-117.
[30] LI Z Y, KOVACHKI N, AZIZZADENESHELI K, et al. Fourier Neural Operator for Parametric Partial Differential Equations[C/OL]. [2024-09-28]. https://arxiv.org/pdf/2010.08895.
[31] DU X Y, YUAN H H, ZHAO P P, et al. Frequency Enhanced Hybrid Attention Network for Sequential Recommendation // Proc of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2023: 78-88.
[32] CAI W L, LIANG Y X, LIU X G, et al. MSGNet: Learning Multi-scale Inter-Series Correlations for Multivariate Time Series Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 2024, 38(10): 11141-11149.
[33] SHI T, HUANG S L. MultiEMO: An Attention-Based Correlation-Aware Multimodal Fusion Framework for Emotion Recognition in Con-
versations // Proc of the 61st Annual Meeting of the Association for Computational Linguistics(Long Papers). Stroudsburg, USA: ACL, 2023: 14752-14766.
[34] LI M C, YANG D K, LEI Y X, et al. A Unified Self-Distillation Framework for Multimodal Sentiment Analysis with Uncertain Mi-ssing Modalities. Proceedings of the AAAI Conference on Artificial Intelligence, 2024, 38(9): 10074-10082.
[35] SANKAR A, ZHANG X Y, KRISHNAN A, et al. Inf-VAE: A Variational Autoencoder Framework to Integrate Homophily and Influence in Diffusion Prediction // Proc of the 13th International Conference on Web Search and Data Mining. New York, USA: ACM, 2020: 510-518.