{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T20:24:41Z","timestamp":1774124681780,"version":"3.50.1"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031434174","type":"print"},{"value":"9783031434181","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-43418-1_5","type":"book-chapter","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T09:02:26Z","timestamp":1694854946000},"page":"70-86","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["CasSampling: Exploring Efficient Cascade Graph Learning for\u00a0Popularity Prediction"],"prefix":"10.1007","author":[{"given":"Guixiang","family":"Cheng","sequence":"first","affiliation":[]},{"given":"Xin","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Shengxiang","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Guangyi","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Xianghua","family":"Miao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,17]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Mishra, S., Rizoiu, M.A., Xie, L.: Modeling popularity in asynchronous social media streams with recurrent neural networks. In: Twelfth International AAAI Conference on Web and Social Media (2018)","DOI":"10.1609\/icwsm.v12i1.15030"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Li, G., Chen, S., Feng, J., Tan, K.l., Li, W.S.: Efficient location-aware influence maximization. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 87\u201398 (2014)","DOI":"10.1145\/2588555.2588561"},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Cao, Q., Shen, H., Cen, K., Ouyang, W., Cheng, X.: Deephawkes: bridging the gap between prediction and understanding of information cascades. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1149\u20131158 (2017)","DOI":"10.1145\/3132847.3132973"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Chen, X., Zhou, F., Zhang, K., Trajcevski, G., Zhong, T., Zhang, F.: Information diffusion prediction via recurrent cascades convolution. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 770\u2013781. IEEE (2019)","DOI":"10.1109\/ICDE.2019.00074"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Tang, X., Liao, D., Huang, W., Xu, J., Zhu, L., Shen, M.: Fully exploiting cascade graphs for real-time forwarding prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 582\u2013590 (2021)","DOI":"10.1609\/aaai.v35i1.16137"},{"key":"5_CR6","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1007\/978-3-030-67664-3_21","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"C Yuan","year":"2021","unstructured":"Yuan, C., Li, J., Zhou, W., Lu, Y., Zhang, X., Hu, S.: DyHGCN: a dynamic heterogeneous graph convolutional network to learn users\u2019 dynamic preferences for information diffusion prediction. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds.) ECML PKDD 2020. LNCS (LNAI), vol. 12459, pp. 347\u2013363. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-67664-3_21"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Cao, Q., Shen, H., Gao, J., Wei, B., Cheng, X.: Popularity prediction on social platforms with coupled graph neural networks. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 70\u201378 (2020)","DOI":"10.1145\/3336191.3371834"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Wu, Z., Zhou, J., Liu, L., Li, C., Gu, F.: Deep popularity prediction in multi-source cascade with HERI-GCN. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE) (2022)","DOI":"10.1109\/ICDE53745.2022.00174"},{"issue":"1","key":"5_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/srep02522","volume":"3","author":"L Weng","year":"2013","unstructured":"Weng, L., Menczer, F., Ahn, Y.Y.: Virality prediction and community structure in social networks. Sci. Rep. 3(1), 1\u20136 (2013)","journal-title":"Sci. Rep."},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Cui, P., Jin, S., Yu, L., Wang, F., Zhu, W., Yang, S.: Cascading outbreak prediction in networks: a data-driven approach. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 901\u2013909 (2013)","DOI":"10.1145\/2487575.2487639"},{"issue":"7","key":"5_CR11","doi-asserted-by":"publisher","first-page":"1399","DOI":"10.1002\/asi.22844","volume":"64","author":"Z Ma","year":"2013","unstructured":"Ma, Z., Sun, A., Cong, G.: On predicting the popularity of newly emerging hashtags in twitter. J. Am. Soc. Inform. Sci. Technol. 64(7), 1399\u20131410 (2013)","journal-title":"J. Am. Soc. Inform. Sci. Technol."},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Petrovic, S., Osborne, M., Lavrenko, V.: Rt to win! predicting message propagation in twitter. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 5, pp. 586\u2013589 (2011)","DOI":"10.1609\/icwsm.v5i1.14149"},{"key":"5_CR13","unstructured":"Shulman, B., Sharma, A., Cosley, D.: Predictability of popularity: gaps between prediction and understanding. In: Tenth International Conference on Web and Social Media (2016)"},{"key":"5_CR14","doi-asserted-by":"crossref","unstructured":"Bao, P., Shen, H.W., Huang, J., Cheng, X.Q.: Popularity prediction in microblogging network: a case study on Sina Weibo. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 177\u2013178 (2013)","DOI":"10.1145\/2487788.2487877"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Cheng, J., Adamic, L., Dow, P.A., Kleinberg, J.M., Leskovec, J.: Can cascades be predicted? In: Proceedings of the 23rd International Conference on World Wide Web, pp. 925\u2013936 (2014)","DOI":"10.1145\/2566486.2567997"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Rizoiu, M.A., Xie, L., Sanner, S., Cebrian, M., Yu, H., Van Hentenryck, P.: Expecting to be hip: hawkes intensity processes for social media popularity. In: Proceedings of the 26th International Conference on World Wide Web, pp. 735\u2013744 (2017)","DOI":"10.1145\/3038912.3052650"},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Mishra, S., Rizoiu, M.A., Xie, L.: Feature driven and point process approaches for popularity prediction. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 1069\u20131078 (2016)","DOI":"10.1145\/2983323.2983812"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Shen, H., Wang, D., Song, C., Barab\u00e1si, A.L.: Modeling and predicting popularity dynamics via reinforced poisson processes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28 (2014)","DOI":"10.1609\/aaai.v28i1.8739"},{"key":"5_CR19","unstructured":"Yang, S.H., Zha, H.: Mixture of mutually exciting processes for viral diffusion. In: International Conference on Machine Learning, pp. 1\u20139. PMLR (2013)"},{"key":"5_CR20","unstructured":"Xu, X., Zhou, F., Zhang, K., Liu, S., Trajcevski, G.: Casflow: exploring hierarchical structures and propagation uncertainty for cascade prediction. IEEE Trans. Knowl. Data Eng. (2021)"},{"key":"5_CR21","unstructured":"Xu, Z., Qian, M., Huang, X., Meng, J.: CasGCN: predicting future cascade growth based on information diffusion graph. arXiv preprint arXiv:2009.05152 (2020)"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Liao, D., Xu, J., Li, G., Huang, W., Liu, W., Li, J.: Popularity prediction on online articles with deep fusion of temporal process and content features. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 200\u2013207 (2019)","DOI":"10.1609\/aaai.v33i01.3301200"},{"key":"5_CR23","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018, accepted as poster). https:\/\/openreview.net\/forum?id=rJXMpikCZ"},{"key":"5_CR24","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"issue":"3","key":"5_CR25","doi-asserted-by":"publisher","first-page":"2584","DOI":"10.1002\/int.22786","volume":"37","author":"X Chen","year":"2022","unstructured":"Chen, X., Zhang, F., Zhou, F., Bonsangue, M.: Multi-scale graph capsule with influence attention for information cascades prediction. Int. J. Intell. Syst. 37(3), 2584\u20132611 (2022)","journal-title":"Int. J. Intell. Syst."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases: Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43418-1_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T13:04:19Z","timestamp":1719407059000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43418-1_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031434174","9783031434181"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43418-1_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The purpose of this paper is to explore efficient and effective methods for learning cascade graphs for popularity prediction while adhering to academic integrity and research ethics requirements. We used publicly available data from social media datasets that have been authorized by Twitter and Weibo officials. To ensure the confidentiality of personal information, all data is anonymized and stored securely. We obtained approval and permission from the ethics committee of our institution to conduct this research.The models and algorithms used in this study are based on publicly available data and previous research results, and we have thoroughly tested and verified them. We commit to conducting a transparent and fair evaluation of the algorithms and models used in this research, and we will present them fully in the paper.Throughout this study, we will adhere to academic standards and ethical requirements, striving to avoid any behavior that may violate these requirements. We hope that this research will contribute to the development of cascade graph learning and popularity prediction, promoting further research in this area.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Statement"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"829","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"196","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.63","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}