{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T07:52:17Z","timestamp":1769845937724,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819556953","type":"print"},{"value":"9789819556960","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\/978-981-95-5696-0_11","type":"book-chapter","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T14:03:06Z","timestamp":1769695386000},"page":"151-165","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SEE-Net: Spectral Environment Encoder for\u00a0Graph Out-of-Distribution Representations Learning"],"prefix":"10.1007","author":[{"given":"Bohan","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Jianming","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Teng","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Jiaqing","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Gori, M., Monfardini, G., Scarselli, F.: A new model for learning in graph domains. In: Proceedings. 2005 IEEE International Joint Conference On Neural Networks 2005, vol. 2 pp. 729-734 (2005)","DOI":"10.1109\/IJCNN.2005.1555942"},{"key":"11_CR2","unstructured":"Chen, M., Wei, Z., Huang, Z., Ding, B., Li, Y.: Simple and deep graph convolutional networks. In: International Conference On Machine Learning. pp. 1725-1735 (2020)"},{"key":"11_CR3","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. Adv. Neural Inform. Process. Syst. 30 (2017)"},{"key":"11_CR4","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":"11_CR5","doi-asserted-by":"crossref","unstructured":"Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: Proceedings of The 15th ACM SIGKDD International Conference On Knowledge Discovery and data mining, pp. 807-816 (2009)","DOI":"10.1145\/1557019.1557108"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Wu, Q., et al.: Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems. In: The World Wide Web Conference, pp. 2091-2102 (2019)","DOI":"10.1145\/3308558.3313442"},{"key":"11_CR7","first-page":"4075","volume":"2023","author":"N Yang","year":"2023","unstructured":"Yang, N., Zeng, K., Wu, Q., Yan, J.: Molerec: combinatorial drug recommendation with substructure-aware molecular representation learning. Proc. ACM Web Conf. 2023, 4075\u20134085 (2023)","journal-title":"Proc. ACM Web Conf."},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Yoon, M., Hooi, B., Shin, K., Faloutsos, C.: Fast and accurate anomaly detection in dynamic graphs with a two-pronged approach. In: Proceedings of the 25th ACM SIGKDD International Conference On Knowledge Discovery and Data Mining, pp. 647-657 (2019)","DOI":"10.1145\/3292500.3330946"},{"key":"11_CR9","unstructured":"Wu, Q., Zhang, H., Yan, J., Wipf, D.: Handling distribution shifts on graphs: An invariance perspective. ArXiv Preprint ArXiv:2202.02466 (2022)"},{"key":"11_CR10","first-page":"12964","volume":"35","author":"N Yang","year":"2022","unstructured":"Yang, N., Zeng, K., Wu, Q., Jia, X., Yan, J.: Learning substructure invariance for out-of-distribution molecular representations. Adv. Neural. Inf. Process. Syst. 35, 12964\u201312978 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"11_CR11","unstructured":"Yehudai, G., Fetaya, E., Meirom, E., Chechik, G., Maron, H.: From local structures to size generalization in graph neural networks. In: International Conference On Machine Learning, pp. 11975-11986 (2021)"},{"key":"11_CR12","unstructured":"Koh, P., et al.: Wilds: a benchmark of in-the-wild distribution shifts. In: International Conference On Machine Learning, pp. 5637-5664 (2021)"},{"key":"11_CR13","unstructured":"Krueger, D., et al.: Out-of-distribution generalization via risk extrapolation (rex). In: International Conference On Machine Learning, pp. 5815-5826 (2021)"},{"key":"11_CR14","first-page":"1048","volume":"34","author":"J Ma","year":"2021","unstructured":"Ma, J., Deng, J., Mei, Q.: Subgroup generalization and fairness of graph neural networks. Adv. Neural. Inf. Process. Syst. 34, 1048\u20131061 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"11_CR15","first-page":"27965","volume":"34","author":"Q Zhu","year":"2021","unstructured":"Zhu, Q., Ponomareva, N., Han, J., Perozzi, B.: Shift-robust gnns: overcoming the limitations of localized graph training data. Adv. Neural. Inf. Process. Syst. 34, 27965\u201327977 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"11_CR16","first-page":"3945","volume":"36","author":"S Gui","year":"2023","unstructured":"Gui, S., Liu, M., Li, X., Luo, Y., Ji, S.: Joint learning of label and environment causal independence for graph out-of-distribution generalization. Adv. Neural. Inf. Process. Syst. 36, 3945\u20133978 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Jia, T., Li, H., Yang, C., Tao, T., Shi, C.: Graph invariant learning with subgraph co-mixup for out-of-distribution generalization. In: Proceedings Of The AAAI Conference On Artificial Intelligence, vol. 38, pp. 8562\u20138570 (2024)","DOI":"10.1609\/aaai.v38i8.28700"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: FLOOD: a flexible invariant learning framework for out-of-distribution generalization on graphs. In: Proceedings Of The 29th ACM SIGKDD Conference On Knowledge Discovery and Data Mining, pp. 1548-1558 (2023)","DOI":"10.1145\/3580305.3599355"},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Wu, Q., Nie, F., Yang, C., Bao, T., Yan, J.: Graph out-of-distribution generalization via causal intervention. In: Proceedings of the ACM Web Conference 2024, pp. 850\u2013860 (2024)","DOI":"10.1145\/3589334.3645604"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Sui, Y., Wang, X., Wu, J., Lin, M., He, X., Chua, T.: Causal attention for interpretable and generalizable graph classification. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1696-1705 (2022)","DOI":"10.1145\/3534678.3539366"},{"key":"11_CR21","unstructured":"Wu, Q., Nie, F., Yang, C., Yan, J. Learning divergence fields for shift-robust graph representations. ArXiv Preprint arXiv:2406.04963 (2024)"},{"key":"11_CR22","unstructured":"Pearl, J. Causalit. Cambridge University Press (2009)"},{"key":"11_CR23","first-page":"25058","volume":"34","author":"H Chang","year":"2021","unstructured":"Chang, H., et al.: Not all low-pass filters are robust in graph convolutional networks. Adv. Neural. Inf. Process. Syst. 34, 25058\u201325071 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Huang, C., Li, H., Zhang, Y., Lei, W., Lv, J.: Cross-space adaptive filter: integrating graph topology and node attributes for alleviating the over-smoothing problem. In: Proceedings of the ACM Web Conference 2024, pp. 803\u2013814 (2024)","DOI":"10.1145\/3589334.3645583"},{"key":"11_CR25","unstructured":"Xu, B., Shen, H., Cao, Q., Qiu, Y., Cheng, X.: Graph wavelet neural network. ArXiv Preprint arXiv:1904.07785 (2019)"},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Liu, R., Yin, R., Liu, Y., Wang, W.: ASWT-SGNN: adaptive spectral wavelet transform-based self-supervised graph neural network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.38, pp. 13990\u201313998 (2024)","DOI":"10.1609\/aaai.v38i12.29307"},{"key":"11_CR27","doi-asserted-by":"crossref","unstructured":"Deb, S., Rahman, S., Rahman, S.: SEA-GWNN: simple and effective adaptive graph wavelet neural network. In: Proceedings of the AAAI Conference On Artificial Intelligence, vol.38, pp. 11740\u201311748 (2024)","DOI":"10.1609\/aaai.v38i10.29058"},{"key":"11_CR28","unstructured":"Kipf, T.. Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv Preprint arXiv:1609.02907 (2016)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5696-0_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T14:03:20Z","timestamp":1769695400000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5696-0_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819556953","9789819556960"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5696-0_11","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":"30 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}