{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T07:02:31Z","timestamp":1780729351280,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":22,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819214617","type":"print"},{"value":"9789819214624","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-92-1462-4_28","type":"book-chapter","created":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T06:46:09Z","timestamp":1780728369000},"page":"354-366","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Subgraph Plug-in Boosts up\u00a0Graph Neural Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9076-4463","authenticated-orcid":false,"given":"Hyung-Jun","family":"Moon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7027-2429","authenticated-orcid":false,"given":"Sung-Bae","family":"Cho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,7]]},"reference":[{"key":"28_CR1","unstructured":"Attali, H., Buscaldi, D., Pernelle, N.: Rewiring techniques to mitigate oversquashing and oversmoothing in GNNs: a survey (2024). arXiv:2411.17429 arXiv preprint"},{"key":"28_CR2","doi-asserted-by":"crossref","unstructured":"Banerjee, P.K., Karhadkar, K., Wang, Y.G., Alon, U., Mont\u00fafar, G.: Oversquashing in GNNs through the lens of information contraction and graph expansion. In: Annual Allerton Conference on Communication, Control, and Computing, pp. 1\u20138 (2022)","DOI":"10.1109\/Allerton49937.2022.9929363"},{"key":"28_CR3","unstructured":"Bevilacqua, B., et al.: Equivariant subgraph aggregation networks (2021). arXiv:2110.02910 arXiv preprint"},{"key":"28_CR4","doi-asserted-by":"crossref","unstructured":"Choi, J., Park, S., Park, S., Cho, S.B., Park, N.: Are graph transformers necessary? Efficient long-range message passing with fractal nodes in MPNNs. arXiv preprint arXiv:2511.13010 (2025)","DOI":"10.1609\/aaai.v40i25.39191"},{"issue":"1","key":"28_CR5","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1038\/s43586-024-00294-7","volume":"4","author":"G Corso","year":"2024","unstructured":"Corso, G., Stark, H., Jegelka, S., Jaakkola, T., Barzilay, R.: Graph neural networks. Nature Rev. Methods Primers 4(1), 17 (2024)","journal-title":"Nature Rev. Methods Primers"},{"key":"28_CR6","first-page":"4776","volume":"35","author":"J Feng","year":"2022","unstructured":"Feng, J., Chen, Y., Li, F., Sarkar, A., Zhang, M.: How powerful are k-hop message passing graph neural networks. Adv. Neural. Inf. Process. Syst. 35, 4776\u20134790 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"28_CR7","first-page":"31376","volume":"35","author":"F Frasca","year":"2022","unstructured":"Frasca, F., Bevilacqua, B., Bronstein, M., Maron, H.: Understanding and extending subgraph GNNs by rethinking their symmetries. Adv. Neural. Inf. Process. Syst. 35, 31376\u201331390 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"28_CR8","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks (2016). arXiv:1609.02907 arXiv preprint"},{"key":"28_CR9","first-page":"12000","volume":"36","author":"L Kong","year":"2023","unstructured":"Kong, L., et al.: Mag-GNN: reinforcement learning boosted graph neural network. Adv. Neural. Inf. Process. Syst. 36, 12000\u201312021 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"28_CR10","unstructured":"Lee, J., Lee, I., Kang, J.: Self-attention graph pooling. In: International Conference on Machine Learning, pp. 3734\u20133743. PMLR (2019)"},{"key":"28_CR11","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.neucom.2023.01.091","volume":"530","author":"HJ Moon","year":"2023","unstructured":"Moon, H.J., Bu, S.J., Cho, S.B.: A graph convolution network with subgraph embedding for mutagenic prediction in aromatic hydrocarbons. Neurocomputing 530, 60\u201368 (2023)","journal-title":"Neurocomputing"},{"key":"28_CR12","doi-asserted-by":"crossref","unstructured":"Moon, H.J., Cho, S.B.: A 4D transformer with spatiotemporal attentions for universal diagnosis of brain disorders. Neurocomputing 132068 (2025)","DOI":"10.1016\/j.neucom.2025.132068"},{"key":"28_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.126799","volume":"272","author":"HJ Moon","year":"2025","unstructured":"Moon, H.J., Cho, S.B.: Traffic prediction by graph transformer embedded with subgraphs. Expert Syst. Appl. 272, 126799 (2025)","journal-title":"Expert Syst. Appl."},{"key":"28_CR14","unstructured":"Oono, K., Suzuki, T.: Graph neural networks exponentially lose expressive power for node classification (2019). arXiv:1905.10947 arXiv preprint"},{"key":"28_CR15","doi-asserted-by":"crossref","unstructured":"Peng, J., Lei, R., Wei, Z.: Beyond over-smoothing: uncovering the trainability challenges in deep graph neural networks. In: ACM International Conference on Information and Knowledge Management, pp. 1878\u20131887 (2024)","DOI":"10.1145\/3627673.3679776"},{"key":"28_CR16","unstructured":"Rong, Y., Huang, W., Xu, T., Huang, J.: DropEdge: towards deep graph convolutional networks on node classification. arXiv preprint arXiv:1907.10903 (2019)"},{"key":"28_CR17","unstructured":"Wijesinghe, A., Wang, Q.: A new perspective on \u201chow graph neural networks go beyond Weisfeiler-Lehman?\u201d. In: International Conference on Learning Representations (2022)"},{"key":"28_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110364","volume":"151","author":"Z Wu","year":"2024","unstructured":"Wu, Z., Chen, Z., Du, S., Huang, S., Wang, S.: Graph convolutional network with elastic topology. Pattern Recogn. 151, 110364 (2024)","journal-title":"Pattern Recogn."},{"key":"28_CR19","doi-asserted-by":"crossref","unstructured":"Xu, J., Zhang, A., Bian, Q., Dwivedi, V.P., Ke, Y.: Union subgraph neural networks. In: AAAI Conference on Artificial Intelligence, vol. 38, pp. 16173\u201316183 (2024)","DOI":"10.1609\/aaai.v38i14.29551"},{"key":"28_CR20","unstructured":"Ying, Z., et al.: Hierarchical graph representation learning with differentiable pooling. Adv. Neural. Inf. Process. Syst. 31 (2018)"},{"key":"28_CR21","unstructured":"Yu, J., Wu, Z., Cai, J., Jia, A.L., Fan, J.: Kernel readout for graph neural networks. In: Int. Joint Conference on Artificial Intelligence, pp. 2505\u20132514 (2024)"},{"key":"28_CR22","unstructured":"Zhao, L., Akoglu, L.: Pairnorm: tackling oversmoothing in GNNs (2019). arXiv:1909.12223 arXiv preprint"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-1462-4_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T06:46:11Z","timestamp":1780728371000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-1462-4_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819214617","9789819214624"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-1462-4_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"7 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hong Kong","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":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 June 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 June 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pakdd2026.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}