{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T05:15:58Z","timestamp":1755839758196,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819755714"},{"type":"electronic","value":"9789819755721"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-5572-1_6","type":"book-chapter","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T23:03:11Z","timestamp":1725058991000},"page":"84-100","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-level Contrastive Learning on\u00a0Weak Social Networks for\u00a0Information Diffusion Prediction"],"prefix":"10.1007","author":[{"given":"Zihan","family":"Feng","sequence":"first","affiliation":[]},{"given":"Rui","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Yajun","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Hong","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xueli","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Qinghua","family":"Hu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,31]]},"reference":[{"key":"6_CR1","unstructured":"Li, J., Yang, Y., Hu, Q., Wang, X., Gao, H.: Public opinion field effect fusion in representation learning for trending topics diffusion. In: NeurIPS (2023)"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Sun L., Rao, Y., Wu, L., Zhang, X., Lan, Y., Nazir, A.: Fighting false information from propagation process: a survey. In: ACM Computing Surveys, vol. 55(10), pp. 1\u201338 (2023)","DOI":"10.1145\/3563388"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Chen, J., Hoops, S., Marathe, A., Mortveit, H., Lewis, B., Venkatramanan, S., et al.: Effective social network-based allocation of COVID-19 vaccines. In: KDD, pp. 1675\u20131683 (2022)","DOI":"10.1145\/3534678.3542673"},{"key":"6_CR4","unstructured":"Broekaert, J. B., La Torre, D., Hafiz, F.: Competing control scenarios in probabilistic SIR epidemics on social-contact networks. In: ArXiv.\/abs\/2108.13714 (2021)"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Cheng, J., Adamic, L., Dow, P., Kleinberg, J. M., Leskovec, J.: Can cascades be predicted? In: WWW, pp. 925\u2013936 (2014)","DOI":"10.1145\/2566486.2567997"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Gao, S., Ma, J., Chen, Z.: Effective and effortless features for popularity prediction in microblogging network. In: WWW, pp. 269\u2013270 (2014)","DOI":"10.1145\/2567948.2577312"},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Wang, J., Zheng, V. W., Liu, Z., Chang, K. C.: Topological recurrent neural network for diffusion prediction. In: ICDM, pp. 475\u2013484 (2017)","DOI":"10.1109\/ICDM.2017.57"},{"key":"6_CR8","unstructured":"Yang, C., Sun, M., Liu, H., Han, S., Liu, Z., Luan, H.: Neural diffusion model for microscopic cascade study. TKDE 33(3), 1128\u20131139 (2021)"},{"key":"6_CR9","doi-asserted-by":"crossref","unstructured":"Wang, Z., Chen, C., Li, W.: A sequential neural information diffusion model with structure attention. In: CIKM, pp. 1795\u20131798 (2018)","DOI":"10.1145\/3269206.3269275"},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"Sankar, A., Zhang, X., Krishnan, A., Han, J.: Inf-VAE: a variational autoencoder framework to integrate homophily and influence in diffusion prediction. In: WSDM, pp. 510\u2013518 (2020)","DOI":"10.1145\/3336191.3371811"},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Yang, C., Tang, J., Sun, M., Cui, G., Liu, Z.: Multi-scale information diffusion prediction with reinforced recurrent networks. In: IJCAI, pp. 4033\u20134039 (2019)","DOI":"10.24963\/ijcai.2019\/560"},{"key":"6_CR12","doi-asserted-by":"crossref","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: ECML\/PKDD, pp. 347\u2013363 (2020)","DOI":"10.1007\/978-3-030-67664-3_21"},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Sun, L., Rao, Y., Zhang, X., Lan, Y., Yu, S.: MS-HGAT: memory-enhanced sequential hypergraph attention network for information diffusion prediction. In: AAAI, pp. 4156\u20134164 (2022)","DOI":"10.1609\/aaai.v36i4.20334"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"Liu, Y., Ding, K., Wang J., Lee, V., Liu, H., Pan, S.: Learning strong graph neural networks with weak information. In: KDD, pp. 1559\u20131571 (2023)","DOI":"10.1145\/3580305.3599410"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Yu, P., Fu, C., Yu, Y., Huang, C., Zhao, Z., Dong, J.: Multiplex heterogeneous graph convolutional network. In: KDD, pp. 2377\u20132387 (2022)","DOI":"10.1145\/3534678.3539482"},{"key":"6_CR16","unstructured":"Hamilton, W. L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: NeurPIS (2017)"},{"key":"6_CR17","unstructured":"Vaswani, A., et al.: Attention is all you need. In: NeurIPS, pp. 5998\u20136008 (2017)"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhu, Y., Wang, C., Ma, W., Li, B., Yu, J.: Adaptive graph representation learning for next POI recommendation. In: WWW, pp. 393\u2013402 (2023)","DOI":"10.1145\/3539618.3591634"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Zhang, H., Yang, Y., Wang, X., Gao, H., Hu, Q.: MLI: A multi-level inference mechanism for user attributes in social networks. In: TOIS, vol. 41(2), 1\u201330 (2022)","DOI":"10.1145\/3545797"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Wang, H., Yang, C., Shi, C.: Neural information diffusion prediction with topic-aware attention network. In: CIKM, pp. 1899\u20131908 (2021)","DOI":"10.1145\/3459637.3482374"},{"key":"6_CR21","unstructured":"Velickovic, P., Fedus, W., Hamilton, W.L., Li\u2018o, P., Bengio, Y., Hjelm, R.D.: Deep graph infomax. In: ICLR (Poster) (2019)"},{"key":"6_CR22","unstructured":"Hjelm, R.D., et al.: Learning deep representations by mutual information estimation and maximization. In: arXiv preprint arXiv:1808.06670 (2018)"},{"key":"6_CR23","unstructured":"You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., Shen, Y.: Graph contrastive learning with augmentations. In: NeurIPS, pp. 5812\u20135823 (2020)"},{"key":"6_CR24","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., Wang, L.: Graph contrastive learning with adaptive augmentation. In: WWW, pp. 2069\u20132080 (2021)","DOI":"10.1145\/3442381.3449802"},{"key":"6_CR25","unstructured":"Hassani, K., Khasahmadi, A. H.: Contrastive multi-view representation learning on graphs. In: ICML, pp. 4116\u20134126 (2022)"},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"An, W., Tian, F., Chen, P., Tang, S., Zheng, Q., Wang, Q.: Fine-grained category discovery under coarse-grained supervision with hierarchical weighted self-contrastive learning. In: EMNLP, pp. 1314\u20131323 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.85"},{"key":"6_CR27","doi-asserted-by":"crossref","unstructured":"Hodas, N.O., Lerman, K.: The simple rules of social contagion. In: Scientific Reports, pp. 1\u20137 (2014)","DOI":"10.1038\/srep04343"},{"key":"6_CR28","doi-asserted-by":"crossref","unstructured":"Zhong, E., Fan, W., Wang, J., Xiao, L., Li, Y.: ComSoc: adaptive transfer of user behaviors over composite social network. In: KDD, pp. 696\u2013704 (2012)","DOI":"10.1145\/2339530.2339641"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-5572-1_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T23:03:49Z","timestamp":1725059029000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5572-1_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819755714","9789819755721"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5572-1_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"31 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gifu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2024a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dasfaa2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}