{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T08:36:33Z","timestamp":1780734993507,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819500086","type":"print"},{"value":"9789819500093","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-95-0009-3_16","type":"book-chapter","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T13:25:08Z","timestamp":1753363508000},"page":"185-196","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Two-View Fusion Graph Neural Networks for Graph Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3236-0508","authenticated-orcid":false,"given":"Zhouhua","family":"Shi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9331-9179","authenticated-orcid":false,"given":"Shiwen","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4361-2568","authenticated-orcid":false,"given":"Guang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4248-8493","authenticated-orcid":false,"given":"Yan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,25]]},"reference":[{"key":"16_CR1","doi-asserted-by":"publisher","first-page":"108218","DOI":"10.1016\/j.patcog.2021.108218","volume":"121","author":"D Cheng","year":"2022","unstructured":"Cheng, D., Yang, F., Xiang, S., Liu, J.: Financial time series forecasting with multi-modality graph neural network. Pattern Recogn. 121, 108218 (2022)","journal-title":"Pattern Recogn."},{"key":"16_CR2","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.inffus.2022.10.006","volume":"91","author":"WC Huang","year":"2023","unstructured":"Huang, W.C., Chen, C.T., Lee, C., Kuo, F.H., Huang, S.H.: Attentive gated graph sequence neural network-based time-series information fusion for financial trading. Inf. Fusion 91, 261\u2013276 (2023)","journal-title":"Inf. Fusion"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Lu, H., Uddin, S.: A weighted patient network-based framework for predicting chronic diseases using graph neural networks. Sci. Rep. 11(1), 22607 (2021)","DOI":"10.1038\/s41598-021-01964-2"},{"key":"16_CR4","doi-asserted-by":"publisher","first-page":"106147","DOI":"10.1016\/j.neunet.2024.106147","volume":"172","author":"K Zheng","year":"2024","unstructured":"Zheng, K., Yu, S., Chen, B.: CI-GNN: a granger causality-inspired graph neural network for interpretable brain network-based psychiatric diagnosis. Neural Netw. 172, 106147 (2024)","journal-title":"Neural Netw."},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Rico, J., Barateiro, J., Oliveira, A.: Graph neural networks for traffic forecasting. arXiv preprint arXiv:2104.13096 (2021)","DOI":"10.70094\/VIIT2597"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Gao, Y., Wang, X.: Survey on traffic flow prediction methods based on graph convolution neural network. In: Proceedings of SPIE, vol. 13064, pp. 130640M-1. SPIE Press, Washington (2024)","DOI":"10.1117\/12.3015682"},{"issue":"5","key":"16_CR7","doi-asserted-by":"publisher","first-page":"2033","DOI":"10.1109\/TKDE.2020.3008732","volume":"34","author":"W Fan","year":"2020","unstructured":"Fan, W., et al.: A graph neural network framework for social recommendations. IEEE Trans. Knowl. Data Eng. 34(5), 2033\u20132047 (2020)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"16_CR8","doi-asserted-by":"publisher","first-page":"106746","DOI":"10.1016\/j.knosys.2021.106746","volume":"214","author":"S Min","year":"2021","unstructured":"Min, S., Gao, Z., Peng, J., Wang, L., Qin, K., Fang, B.: Stgsn\u2014a spatial\u2013temporal graph neural network framework for time-evolving social networks. Knowl.-Based Syst. 214, 106746 (2021)","journal-title":"Knowl.-Based Syst."},{"issue":"1","key":"16_CR9","doi-asserted-by":"publisher","first-page":"e1010812","DOI":"10.1371\/journal.pcbi.1010812","volume":"19","author":"M Ma","year":"2023","unstructured":"Ma, M., Lei, X.: A dual graph neural network for drug\u2013drug interactions prediction based on molecular structure and interactions. PLoS Comput. Biol. 19(1), e1010812 (2023)","journal-title":"PLoS Comput. Biol."},{"key":"16_CR10","doi-asserted-by":"publisher","first-page":"108869","DOI":"10.1016\/j.compbiomed.2024.108869","volume":"180","author":"Z Zhou","year":"2024","unstructured":"Zhou, Z., et al.: A novel graph neural network method for Alzheimer\u2019s disease classification. Comput. Biol. Med. 180, 108869 (2024)","journal-title":"Comput. Biol. Med."},{"key":"16_CR11","doi-asserted-by":"publisher","first-page":"120594","DOI":"10.1016\/j.neuroimage.2024.120594","volume":"292","author":"K Zheng","year":"2024","unstructured":"Zheng, K., Yu, S., Chen, L., Dang, L., Chen, B.: BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping. Neuroimage 292, 120594 (2024)","journal-title":"Neuroimage"},{"issue":"4","key":"16_CR12","doi-asserted-by":"publisher","first-page":"150","DOI":"10.3390\/info10040150","volume":"10","author":"K Kowsari","year":"2019","unstructured":"Kowsari, K., Jafari Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L., Brown, D.: Text classification algorithms: a survey. Information 10(4), 150 (2019)","journal-title":"Information"},{"key":"16_CR13","unstructured":"Kipf, T. N., & Welling, M.: Semi-supervised classification with graph convolutional networks.\u00a0arXiv preprint arXiv:1609.02907 (2016)"},{"key":"16_CR14","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)"},{"key":"16_CR15","doi-asserted-by":"publisher","first-page":"106784","DOI":"10.1016\/j.neunet.2024.106784","volume":"181","author":"P Quan","year":"2025","unstructured":"Quan, P., Zheng, L., Zhang, W., Xiao, Y., Niu, L., Shi, Y.: ExGAT: context extended graph attention neural network. Neural Netw. 181, 106784 (2025)","journal-title":"Neural Netw."},{"key":"16_CR16","unstructured":"Kesimoglu, Z. N., Bozdag, S.: GRAF: graph attention-aware fusion networks. arXiv preprint arXiv:2303.16781 (2023)"},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Yao, T., Sun, J., Cao, D., Zhang, K., Chen, G.: MuGSI: distilling GNNs with multi-granularity structural information for graph classification. In: Proceedings of the ACM Web Conference 2024, pp. 709\u2013720. ACM Press, New York (2024)","DOI":"10.1145\/3589334.3645542"},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Verma, V., et al.: GraphMix: improved training of GNNs for semi-supervised learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 11, pp. 10024\u201310032. AAAI Press, Menlo Park (2021)","DOI":"10.1609\/aaai.v35i11.17203"},{"key":"16_CR19","unstructured":"Han, X., Jiang, Z., Liu, N., Hu, X.: G-mixup: graph data augmentation for graph classification. In: International Conference on Machine Learning, pp. 8230\u20138248. PMLR Press, New York (2022)"},{"key":"16_CR20","unstructured":"Sun, J., Zhang, L., Chen, G., Xu, P., Zhang, K., Yang, Y.: Feature expansion for graph neural networks. In: International Conference on Machine Learning, pp. 33156\u201333176. PMLR Press, New York (2023)"},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Simonovsky, M., Komodakis, N.: GraphVAE: towards generation of small graphs using variational autoencoders. In: Artificial Neural Networks and Machine Learning\u2013ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, 4\u20137 October 2018, Proceedings, Part I 27, pp. 412\u2013422. Springer (2018)","DOI":"10.1007\/978-3-030-01418-6_41"},{"issue":"4","key":"16_CR22","doi-asserted-by":"publisher","first-page":"4077","DOI":"10.1109\/TKDE.2022.3142179","volume":"35","author":"X Zhao","year":"2022","unstructured":"Zhao, X., et al.: Multi-view tensor graph neural networks through reinforced aggregation. IEEE Trans. Knowl. Data Eng. 35(4), 4077\u20134091 (2022)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"3","key":"16_CR23","first-page":"2220","volume":"35","author":"B Liu","year":"2021","unstructured":"Liu, B., Che, Z., Zhong, H., Xiao, Y.: A ranking based multi-view method for positive and unlabeled graph classification. IEEE Trans. Knowl. Data Eng. 35(3), 2220\u20132230 (2021)","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-0009-3_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T02:22:38Z","timestamp":1779416558000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-0009-3_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819500086","9789819500093"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-0009-3_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"25 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}