{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T06:50:18Z","timestamp":1743058218559,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819794393"},{"type":"electronic","value":"9789819794409"}],"license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"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\/978-981-97-9440-9_36","type":"book-chapter","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T17:06:34Z","timestamp":1730394394000},"page":"469-482","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Joint Graph Augmentation and\u00a0Adaptive Synthetic Sampling for\u00a0Imbalanced Node Classification"],"prefix":"10.1007","author":[{"given":"Guangquan","family":"Lu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanxin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yadan","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiamin","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Faliang","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,1]]},"reference":[{"key":"36_CR1","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1007\/978-3-319-71249-9_46","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"S Ando","year":"2017","unstructured":"Ando, S., Huang, C.Y.: Deep over-sampling framework for classifying imbalanced data. In: Ceci, M., Hollm\u00e9n, J., Todorovski, L., Vens, C., D\u017eeroski, S. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10534, pp. 770\u2013785. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-71249-9_46"},{"key":"36_CR2","doi-asserted-by":"crossref","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 321\u2013357 (2002)","DOI":"10.1613\/jair.953"},{"key":"36_CR3","doi-asserted-by":"crossref","unstructured":"Gasteiger, J., Bojchevski, A., G\u00fcnnemann, S.: Predict then propagate: graph neural networks meet personalized pagerank (2019)","DOI":"10.1145\/3394486.3403296"},{"key":"36_CR4","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, vol.\u00a030. Curran Associates, Inc. (2017)"},{"key":"36_CR5","unstructured":"Hassani, K., Khasahmadi, A.H.: Contrastive multi-view representation learning on graphs. In: Proceedings of the 37th International Conference on Machine Learning. ICML\u201920, JMLR.org (2020)"},{"key":"36_CR6","doi-asserted-by":"crossref","unstructured":"He, H., Bai, Y., Garcia, E.A., Li, S.: Adasyn: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Network, pp. 1322\u20131328 (2008)","DOI":"10.1109\/IJCNN.2008.4633969"},{"key":"36_CR7","unstructured":"Khoshraftar, S., An, A.: A survey on graph representation learning methods (2022)"},{"key":"36_CR8","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations. ICLR \u201917 (2017)"},{"key":"36_CR9","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.ins.2023.02.092","volume":"632","author":"J Li","year":"2023","unstructured":"Li, J., Lu, G., Wu, Z., Ling, F.: Multi-view representation model based on graph autoencoder. Inf. Sci. 632, 439\u2013453 (2023)","journal-title":"Inf. Sci."},{"key":"36_CR10","doi-asserted-by":"crossref","unstructured":"Li, R., Wang, S., Zhu, F., Huang, J.: Adaptive graph convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1 (2018)","DOI":"10.1609\/aaai.v32i1.11691"},{"key":"36_CR11","doi-asserted-by":"crossref","unstructured":"Li, W.Z., Wang, C.D., Xiong, H., Lai, J.H.: Graphsha: synthesizing harder samples for class-imbalanced node classification. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. KDD \u201923 (2023)","DOI":"10.1145\/3580305.3599374"},{"key":"36_CR12","unstructured":"Li, Y., Yu, R., Shahabi, C., Liu, Y.: Graph convolutional recurrent neural network: data-driven traffic forecasting (2017)"},{"key":"36_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: Pick and choose: a GNN-based imbalanced learning approach for fraud detection. In: Proceedings of the Web Conference 2021 (2021)","DOI":"10.1145\/3442381.3449989"},{"issue":"4","key":"36_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.102953","volume":"59","author":"G Lu","year":"2022","unstructured":"Lu, G., Li, J., Wei, J.: Aspect sentiment analysis with heterogeneous graph neural networks. Inf. Process. Manage. 59(4), 102953 (2022)","journal-title":"Inf. Process. Manage."},{"key":"36_CR15","unstructured":"Mernyei, P., Cangea, C.: Wiki-CS: a Wikipedia-based benchmark for graph neural networks (2020)"},{"key":"36_CR16","unstructured":"More, A.: Survey of resampling techniques for improving classification performance in unbalanced datasets (2016)"},{"key":"36_CR17","unstructured":"Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. In: The Web Conference (1999)"},{"key":"36_CR18","doi-asserted-by":"crossref","unstructured":"Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., Zhang, C.: Adversarially regularized graph autoencoder for graph embedding. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 2609\u20132615. IJCAI\u201918. AAAI Press (2018)","DOI":"10.24963\/ijcai.2018\/362"},{"key":"36_CR19","unstructured":"Park, J., Song, J., Yang, E.: GraphENS: neighbor-aware ego network synthesis for class-imbalanced node classification. In: International Conference on Learning Representations (2022)"},{"key":"36_CR20","doi-asserted-by":"crossref","unstructured":"Qu, L., Zhu, H., Zheng, R., Shi, Y., Yin, H.: Imgagn: imbalanced network embedding via generative adversarial graph networks. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (2021)","DOI":"10.1145\/3447548.3467334"},{"key":"36_CR21","doi-asserted-by":"crossref","unstructured":"Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., Eliassi-Rad, T.: Collective classification in network data. In: The AI Magazine (2008)","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"36_CR22","unstructured":"Shchur, O., Mumme, M., Bojchevski, A., G\u00fcnnemann, S.: Pitfalls of graph neural network evaluation (2018)"},{"key":"36_CR23","doi-asserted-by":"crossref","unstructured":"Tang, L., Liu, H.: Relational learning via latent social dimensions. In: ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2009)","DOI":"10.1145\/1557019.1557109"},{"key":"36_CR24","doi-asserted-by":"crossref","unstructured":"Wanda, P., Jie, H.: Deepfriend: finding abnormal nodes in online social networks using dynamic deep learning. Social Network Analysis and Mining (2021)","DOI":"10.1007\/s13278-021-00742-2"},{"key":"36_CR25","doi-asserted-by":"crossref","unstructured":"Wu, L., Xia, J., Gao, Z., Lin, H., Tan, C., Li, S.Z.: Graphmixup: improving class-imbalanced node classification by reinforcement mixup and self-supervised context prediction. In: Machine Learning and Knowledge Discovery in Databases (2023)","DOI":"10.1007\/978-3-031-26412-2_32"},{"key":"36_CR26","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? (2018)"},{"key":"36_CR27","doi-asserted-by":"crossref","unstructured":"Yuan, B., Ma, X.: Sampling + reweighting: Boosting the performance of adaboost on imbalanced datasets. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20136 (2012)","DOI":"10.1109\/IJCNN.2012.6252738"},{"key":"36_CR28","doi-asserted-by":"crossref","unstructured":"Zeng, L., Li, L., Gao, Z., Zhao, P., Li, J.: Imgcl: Revisiting graph contrastive learning on imbalanced node classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 9, pp. 11138\u201311146 (2023)","DOI":"10.1609\/aaai.v37i9.26319"},{"key":"36_CR29","doi-asserted-by":"crossref","unstructured":"Zhao, T., Zhang, X., Wang, S.: GraphSMOTE: imbalanced node classification on graphs with graph neural networks. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining. ACM (2021)","DOI":"10.1145\/3437963.3441720"},{"key":"36_CR30","doi-asserted-by":"crossref","unstructured":"Zhou, M., Gong, Z.: GraphSR: a data augmentation algorithm for imbalanced node classification. In: AAAI Conference on Artificial Intelligence (2023)","DOI":"10.1609\/aaai.v37i4.25622"}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Chinese Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-9440-9_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T17:12:50Z","timestamp":1730394770000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-9440-9_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,1]]},"ISBN":["9789819794393","9789819794409"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-9440-9_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,1]]},"assertion":[{"value":"1 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NLPCC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF International Conference on Natural Language Processing and Chinese Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hangzhou","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nlpcc2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tcci.ccf.org.cn\/conference\/2024\/index.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}