{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T17:48:04Z","timestamp":1759513684367,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819755516"},{"type":"electronic","value":"9789819755523"}],"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\/978-981-97-5552-3_32","type":"book-chapter","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T07:04:15Z","timestamp":1727679855000},"page":"474-490","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["FedSig: A Federated Graph Augmentation for\u00a0Class-Imbalanced Node Classification"],"prefix":"10.1007","author":[{"given":"Bei","family":"Bi","sequence":"first","affiliation":[]},{"given":"Zhiwei","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Pengpeng","family":"Qiao","sequence":"additional","affiliation":[]},{"given":"Ye","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Guoren","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,1]]},"reference":[{"key":"32_CR1","doi-asserted-by":"publisher","first-page":"1937","DOI":"10.1007\/s11063-018-09977-1","volume":"50","author":"YS Aurelio","year":"2019","unstructured":"Aurelio, Y.S., De Almeida, G.M., de Castro, C.L., Braga, A.P.: Learning from imbalanced data sets with weighted cross-entropy function. Neural Process. Lett. 50, 1937\u20131949 (2019)","journal-title":"Neural Process. Lett."},{"key":"32_CR2","unstructured":"Bojchevski, A., G\u00fcnnemann, S.: Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking. arXiv preprint arXiv:1707.03815 (2017)"},{"key":"32_CR3","doi-asserted-by":"crossref","unstructured":"Bunkhumpornpat, C., Sinapiromsaran, K., Lursinsap, C.: Safe-level-smote: safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. In: PAKDD, pp. 475\u2013482. Springer (2009)","DOI":"10.1007\/978-3-642-01307-2_43"},{"key":"32_CR4","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"issue":"1","key":"32_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1007730.1007733","volume":"6","author":"NV Chawla","year":"2004","unstructured":"Chawla, N.V., Japkowicz, N., Kotcz, A.: Special issue on learning from imbalanced data sets. ACM SIGKDD Explorations Newsl 6(1), 1\u20136 (2004)","journal-title":"ACM SIGKDD Explorations Newsl"},{"key":"32_CR6","doi-asserted-by":"crossref","unstructured":"Chen, H., Frikha, A., Krompass, D., Tresp, V.: Fraug: tackling federated learning with non-iid features via representation augmentation. arXiv preprint arXiv:2205.14900 (2022)","DOI":"10.1109\/ICCV51070.2023.00447"},{"issue":"1","key":"32_CR7","first-page":"59","volume":"32","author":"M Duan","year":"2020","unstructured":"Duan, M., Liu, D., Chen, X., Liu, R., Tan, Y., Liang, L.: Self-balancing federated learning with global imbalanced data in mobile systems. TPDS 32(1), 59\u201371 (2020)","journal-title":"TPDS"},{"key":"32_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102272","volume":"75","author":"M Ghorbani","year":"2022","unstructured":"Ghorbani, M., Kazi, A., Baghshah, M.S., Rabiee, H.R., Navab, N.: Ra-gcn: graph convolutional network for disease prediction problems with imbalanced data. Med. Image Anal. 75, 102272 (2022)","journal-title":"Med. Image Anal."},{"key":"32_CR9","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. Advances in neural information processing systems 30 (2017)"},{"key":"32_CR10","doi-asserted-by":"crossref","unstructured":"Li, B., Liu, Y., Wang, X.: Gradient harmonized single-stage detector. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a033, pp. 8577\u20138584 (2019)","DOI":"10.1609\/aaai.v33i01.33018577"},{"key":"32_CR11","doi-asserted-by":"crossref","unstructured":"Li, Q., He, B., Song, D.: Model-contrastive federated learning. In: CVPR, pp. 10713\u201310722 (2021)","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"32_CR12","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: ICCV, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"32_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Y., Ao, X., Qin, Z., Chi, J., Feng, J., Yang, H., He, Q.: Pick and choose: a gnn-based imbalanced learning approach for fraud detection. In: Proceedings of the Web Conference 2021, pp. 3168\u20133177 (2021)","DOI":"10.1145\/3442381.3449989"},{"issue":"11","key":"32_CR14","doi-asserted-by":"publisher","first-page":"1799","DOI":"10.3390\/math10111799","volume":"10","author":"Y Liu","year":"2022","unstructured":"Liu, Y., Zhang, Z., Liu, Y., Zhu, Y.: Gatsmote: improving imbalanced node classification on graphs via attention and homophily. Mathematics 10(11), 1799 (2022)","journal-title":"Mathematics"},{"key":"32_CR15","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: SIGKDD, pp. 1390\u20131398 (2021)","DOI":"10.1145\/3447548.3467334"},{"key":"32_CR16","doi-asserted-by":"crossref","unstructured":"Rozemberczki, B., Allen, C., Sarkar, R.: Multi-scale attributed node embedding. J. Complex Networks 9(2), cnab014 (2021)","DOI":"10.1093\/comnet\/cnab014"},{"key":"32_CR17","unstructured":"Sarkar, D., Narang, A., Rai, S.: Fed-focal loss for imbalanced data classification in federated learning. arXiv preprint arXiv:2011.06283 (2020)"},{"key":"32_CR18","unstructured":"Shchur, O., Mumme, M., Bojchevski, A., G\u00fcnnemann, S.: Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868 (2018)"},{"key":"32_CR19","doi-asserted-by":"crossref","unstructured":"Shi, M., Tang, Y., Zhu, X., Wilson, D., Liu, J.: Multi-class imbalanced graph convolutional network learning. In: IJCAI-20 (2020)","DOI":"10.24963\/ijcai.2020\/398"},{"key":"32_CR20","doi-asserted-by":"crossref","unstructured":"Shuai, X., Shen, Y., Jiang, S., Zhao, Z., Yan, Z., Xing, G.: Balancefl: Addressing class imbalance in long-tail federated learning. In: IPSN, pp. 271\u2013284. IEEE (2022)","DOI":"10.1109\/IPSN54338.2022.00029"},{"key":"32_CR21","doi-asserted-by":"crossref","unstructured":"Tan, J., Wang, C., Li, B., Li, Q., Ouyang, W., Yin, C., Yan, J.: Equalization loss for long-tailed object recognition. In: CVPR, pp. 11662\u201311671 (2020)","DOI":"10.1109\/CVPR42600.2020.01168"},{"key":"32_CR22","doi-asserted-by":"crossref","unstructured":"Wang, Y., Tong, Y., Zhou, Z., Zhang, R., Pan, S.J., Fan, L., Yang, Q.: Distribution-regularized federated learning on non-iid data. In: 2023 IEEE 39th International Conference on Data Engineering (ICDE), pp. 2113\u20132125. IEEE (2023)","DOI":"10.1109\/ICDE55515.2023.00164"},{"issue":"1","key":"32_CR23","first-page":"86","volume":"8","author":"Y Wang","year":"2021","unstructured":"Wang, Y., Gui, G., Gacanin, H., Adebisi, B., Sari, H., Adachi, F.: Federated learning for automatic modulation classification under class imbalance and varying noise condition. TCCN 8(1), 86\u201396 (2021)","journal-title":"TCCN"},{"key":"32_CR24","doi-asserted-by":"crossref","unstructured":"Yang, M., Wang, X., Zhu, H., Wang, H., Qian, H.: Federated learning with class imbalance reduction. In: EUSIPCO, pp. 2174\u20132178 (2021)","DOI":"10.23919\/EUSIPCO54536.2021.9616052"},{"key":"32_CR25","doi-asserted-by":"crossref","unstructured":"Zhao, T., Zhang, X., Wang, S.: Graphsmote: Imbalanced node classification on graphs with graph neural networks. In: WSDM, pp. 833\u2013841 (2021)","DOI":"10.1145\/3437963.3441720"}],"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\/978-981-97-5552-3_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T07:14:44Z","timestamp":1727680484000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5552-3_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819755516","9789819755523"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5552-3_32","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":"1 October 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"}}]}}