{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T10:00:17Z","timestamp":1743156017940,"version":"3.40.3"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030675363"},{"type":"electronic","value":"9783030675370"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-67537-0_5","type":"book-chapter","created":{"date-parts":[[2021,1,21]],"date-time":"2021-01-21T13:12:57Z","timestamp":1611234777000},"page":"64-79","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CPNSA: Cascade Prediction with Network Structure Attention"],"prefix":"10.1007","author":[{"given":"Chaochao","family":"Liu","sequence":"first","affiliation":[]},{"given":"Wenjun","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Pengfei","family":"Jiao","sequence":"additional","affiliation":[]},{"given":"Yueheng","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Xiaoming","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xue","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,22]]},"reference":[{"key":"5_CR1","unstructured":"Artzi, Y., Pantel, P., Gamon, M.: Predicting responses to microblog posts. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 602\u2013606. Association for Computational Linguistics (2012)"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Bakshy, E., Rosenn, I., Marlow, C., Adamic, L.: The role of social networks in information diffusion. In: Proceedings of the 21st International Conference on World Wide Web, pp. 519\u2013528 (2012)","DOI":"10.1145\/2187836.2187907"},{"issue":"3","key":"5_CR3","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/s10115-013-0646-6","volume":"37","author":"N Barbieri","year":"2013","unstructured":"Barbieri, N., Bonchi, F., Manco, G.: Topic-aware social influence propagation models. Knowl. Inf. Syst. 37(3), 555\u2013584 (2013). https:\/\/doi.org\/10.1007\/s10115-013-0646-6","journal-title":"Knowl. Inf. Syst."},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Cataldi, M., Di Caro, L., Schifanella, C.: Emerging topic detection on twitter based on temporal and social terms evaluation. In: Proceedings of the Tenth International Workshop on Multimedia Data Mining, p. 4. ACM (2010)","DOI":"10.1145\/1814245.1814249"},{"issue":"5996","key":"5_CR5","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1126\/science.1185231","volume":"329","author":"D Centola","year":"2010","unstructured":"Centola, D.: The spread of behavior in an online social network experiment. Science 329(5996), 1194\u20131197 (2010)","journal-title":"Science"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Cheng, S., Shen, H., Huang, J., Zhang, G., Cheng, X.: Staticgreedy: solving the scalability-accuracy dilemma in influence maximization. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, pp. 509\u2013518. ACM (2013)","DOI":"10.1145\/2505515.2505541"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Du, N., Dai, H., Trivedi, R., Upadhyay, U., Gomez-Rodriguez, M., Song, L.: Recurrent marked temporal point processes: Embedding event history to vector. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1555\u20131564. ACM (2016)","DOI":"10.1145\/2939672.2939875"},{"key":"5_CR8","unstructured":"Gomez-Rodriguez, M., Leskovec, J., Sch\u00f6lkopf, B.: Modeling information propagation with survival theory. In: International Conference on Machine Learning, pp. 666\u2013674 (2013)"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: Proceedings of the third ACM International Conference on Web Search and Data Mining, pp. 241\u2013250. ACM (2010)","DOI":"10.1145\/1718487.1718518"},{"key":"5_CR10","unstructured":"Henaff, M., Szlam, A., LeCun, Y.: Recurrent orthogonal networks and long-memory tasks. arXiv preprint arXiv:1602.06662 (2016)"},{"issue":"1","key":"5_CR11","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1140\/epjds5","volume":"1","author":"T Hogg","year":"2012","unstructured":"Hogg, T., Lerman, K.: Social dynamics of digg. EPJ Data Sci. 1(1), 5 (2012)","journal-title":"EPJ Data Sci."},{"issue":"6","key":"5_CR12","doi-asserted-by":"publisher","first-page":"2137","DOI":"10.1007\/s10489-018-1387-8","volume":"49","author":"H Huang","year":"2019","unstructured":"Huang, H., Shen, H., Meng, Z., Chang, H., He, H.: Community-based influence maximization for viral marketing. Appl. Intell. 49(6), 2137\u20132150 (2019). https:\/\/doi.org\/10.1007\/s10489-018-1387-8","journal-title":"Appl. Intell."},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Kempe, D., Kleinberg, J., Tardos, \u00c9.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137\u2013146. ACM (2003)","DOI":"10.1145\/956750.956769"},{"key":"5_CR14","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"4","key":"5_CR15","doi-asserted-by":"publisher","first-page":"046110","DOI":"10.1103\/PhysRevE.78.046110","volume":"78","author":"A Lancichinetti","year":"2008","unstructured":"Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78(4), 046110 (2008)","journal-title":"Phys. Rev. E"},{"key":"5_CR16","first-page":"985","volume":"11","author":"J Leskovec","year":"2010","unstructured":"Leskovec, J., Chakrabarti, D., Kleinberg, J., Faloutsos, C., Ghahramani, Z.: Kronecker graphs: an approach to modeling networks. J. Mach. Learn. Res. 11, 985\u20131042 (2010)","journal-title":"J. Mach. Learn. Res."},{"issue":"6","key":"5_CR17","doi-asserted-by":"publisher","first-page":"1296","DOI":"10.1007\/s11704-018-7453-x","volume":"13","author":"K Li","year":"2019","unstructured":"Li, K., Lv, G., Wang, Z., Liu, Q., Chen, E., Qiao, L.: Understanding the mechanism of social tie in the propagation process of social network with communication channel. Front. Comput. Sci. 13(6), 1296\u20131308 (2019). https:\/\/doi.org\/10.1007\/s11704-018-7453-x","journal-title":"Front. Comput. Sci."},{"key":"5_CR18","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.neucom.2019.05.069","volume":"359","author":"C Liu","year":"2019","unstructured":"Liu, C., Wang, W., Sun, Y.: Community structure enhanced cascade prediction. Neurocomputing 359, 276\u2013284 (2019)","journal-title":"Neurocomputing"},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"Liu, S., Zheng, H., Shen, H., Cheng, X., Liao, X.: Learning concise representations of users\u2019 influences through online behaviors. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17, pp. 2351\u20132357 (2017)","DOI":"10.24963\/ijcai.2017\/327"},{"key":"5_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1007\/978-3-642-35063-4_65","volume-title":"Web Information Systems Engineering - WISE 2012","author":"Z Luo","year":"2012","unstructured":"Luo, Z., Wang, Y., Wu, X.: Predicting retweeting behavior based on autoregressive moving average model. In: Wang, X.S., Cruz, I., Delis, A., Huang, G. (eds.) WISE 2012. LNCS, vol. 7651, pp. 777\u2013782. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-35063-4_65"},{"issue":"8","key":"5_CR21","doi-asserted-by":"publisher","first-page":"088701","DOI":"10.1103\/PhysRevLett.113.088701","volume":"113","author":"A Nematzadeh","year":"2014","unstructured":"Nematzadeh, A., Ferrara, E., Flammini, A., Ahn, Y.Y.: Optimal network modularity for information diffusion. Phys. Rev. Lett. 113(8), 088701 (2014)","journal-title":"Phys. Rev. Lett."},{"issue":"8","key":"5_CR22","doi-asserted-by":"publisher","first-page":"1127","DOI":"10.1080\/1369118X.2015.1104372","volume":"19","author":"JY Nip","year":"2016","unstructured":"Nip, J.Y., Fu, K.W.: Networked framing between source posts and their reposts: an analysis of public opinion on china\u2019s microblogs. Inf. Commun. Soc. 19(8), 1127\u20131149 (2016)","journal-title":"Inf. Commun.. Soc."},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Peng, H.K., Zhu, J., Piao, D., Yan, R., Zhang, Y.: Retweet modeling using conditional random fields. In: 2011 11th IEEE International Conference on Data Mining Workshops, pp. 336\u2013343. IEEE (2011)","DOI":"10.1109\/ICDMW.2011.146"},{"key":"5_CR24","doi-asserted-by":"crossref","unstructured":"Qiu, J., Tang, J., Ma, H., Dong, Y., Wang, K., Tang, J.: DeepInf: modeling influence locality in large social networks. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018) (2018)","DOI":"10.1145\/3219819.3220077"},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"Stern, D.H., Herbrich, R., Graepel, T.: Matchbox: large scale online bayesian recommendations. In: Proceedings of the 18th International Conference on World Wide Web, pp. 111\u2013120. ACM (2009)","DOI":"10.1145\/1526709.1526725"},{"issue":"5","key":"5_CR26","doi-asserted-by":"publisher","first-page":"053053","DOI":"10.1088\/1367-2630\/aac0c9","volume":"20","author":"Z Su","year":"2018","unstructured":"Su, Z., Wang, W., Li, L., Stanley, H.E., Braunstein, L.A.: Optimal community structure for social contagions. New J. Phys. 20(5), 053053 (2018)","journal-title":"New J. Phys."},{"key":"5_CR27","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067\u20131077. International World Wide Web Conferences Steering Committee (2015)","DOI":"10.1145\/2736277.2741093"},{"key":"5_CR28","doi-asserted-by":"crossref","unstructured":"Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1225\u20131234. ACM (2016)","DOI":"10.1145\/2939672.2939753"},{"key":"5_CR29","doi-asserted-by":"crossref","unstructured":"Wang, Y., Shen, H., Liu, S., Gao, J., Cheng, X.: Cascade dynamics modeling with attention-based recurrent neural network. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence,pp. 2985\u20132991. AAAI Press (2017)","DOI":"10.24963\/ijcai.2017\/416"},{"key":"5_CR30","doi-asserted-by":"crossref","unstructured":"Wang, Z., Chen, C., Li, W.: A sequential neural information diffusion model with structure attention. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 1795\u20131798. ACM (2018)","DOI":"10.1145\/3269206.3269275"},{"key":"5_CR31","doi-asserted-by":"crossref","unstructured":"Weng, L., Menczer, F., Ahn, Y.Y.: Predicting successful memes using network and community structure. In: ICWSM (2014)","DOI":"10.1609\/icwsm.v8i1.14530"},{"issue":"08","key":"5_CR32","doi-asserted-by":"publisher","first-page":"1650092","DOI":"10.1142\/S0129183116500923","volume":"27","author":"J Wu","year":"2016","unstructured":"Wu, J., Du, R., Zheng, Y., Liu, D.: Optimal multi-community network modularity for information diffusion. Int. J. Modern Phys. C 27(08), 1650092 (2016)","journal-title":"Int. J. Modern Phys. C"},{"key":"5_CR33","unstructured":"Wu, Q., Burges, C.J., Svore, K.M., Gao, J.: Ranking, boosting, and model adaptation. Technical report, Microsoft Research (2008)"},{"key":"5_CR34","doi-asserted-by":"crossref","unstructured":"Xiao, S., Yan, J., Yang, X., Zha, H., Chu, S.M.: Modeling the intensity function of point process via recurrent neural networks. In: AAAI, vol. 17, pp. 1597\u20131603 (2017)","DOI":"10.1609\/aaai.v31i1.10724"},{"key":"5_CR35","doi-asserted-by":"crossref","unstructured":"Xu, H., Wu, W., Nemati, S., Zha, H.: Patient flow prediction via discriminative learning of mutually-correcting processes. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 37\u201338. IEEE (2017)","DOI":"10.1109\/ICDE.2017.25"},{"key":"5_CR36","doi-asserted-by":"crossref","unstructured":"Yang, Z., Guo, J., Cai, K., Tang, J., Li, J., Zhang, L., Su, Z.: Understanding retweeting behaviors in social networks. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1633\u20131636. ACM (2010)","DOI":"10.1145\/1871437.1871691"},{"issue":"1","key":"5_CR37","first-page":"59","volume":"11","author":"S Ye","year":"2013","unstructured":"Ye, S., Wu, F.: Measuring message propagation and social influence on twitter.com. Int. J. Commun. Netw. Distrib. Syst. 11(1), 59\u201376 (2013)","journal-title":"Int. J. Commun. Netw. Distrib. Syst."},{"key":"5_CR38","unstructured":"Zaman, T.R., Herbrich, R., Van Gael, J., Stern, D.: Predicting information spreading in twitter. In: Workshop on computational Social Science and the Wisdom of Crowds, nips, vol. 104, pp. 17599\u2013601. Citeseer (2010)"},{"key":"5_CR39","unstructured":"Zhang, J., Liu, B., Tang, J., Chen, T., Li, J.: Social influence locality for modeling retweeting behaviors. In: IJCAI, vol. 13, pp. 2761\u20132767 (2013)"},{"key":"5_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Gong, Y., Wu, J., Huang, H., Huang, X.: Retweet prediction with attention-based deep neural network. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 75\u201384. ACM (2016)","DOI":"10.1145\/2983323.2983809"},{"key":"5_CR41","unstructured":"Zhou, K., Zha, H., Song, L.: Learning social infectivity in sparse low-rank networks using multi-dimensional Hawkes processes. In: Artificial Intelligence and Statistics, pp. 641\u2013649 (2013)"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Collaborative Computing: Networking, Applications and Worksharing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-67537-0_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T16:29:44Z","timestamp":1670862584000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-67537-0_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030675363","9783030675370"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-67537-0_5","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"22 January 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CollaborateCom","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Collaborative Computing: Networking, Applications and Worksharing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"colcom2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/collaboratecom.eai-conferences.org\/2020\/","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":"Confy+","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"211","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":"61","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":"16","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":"29% - 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","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to COVID-19 pandemic.","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)"}}]}}