{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:41:26Z","timestamp":1757619686388,"version":"3.44.0"},"publisher-location":"Singapore","reference-count":36,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819699209"},{"type":"electronic","value":"9789819699216"}],"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\/978-981-96-9921-6_3","type":"book-chapter","created":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T06:14:49Z","timestamp":1753424089000},"page":"27-38","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FedPKA: Federated Graph-Level Clustering Network with Personalized Knowledge Aggregation"],"prefix":"10.1007","author":[{"given":"Junlong","family":"Wu","sequence":"first","affiliation":[]},{"given":"Haotian","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jingxin","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Wenxuan","family":"Tu","sequence":"additional","affiliation":[]},{"given":"Renda","family":"Han","sequence":"additional","affiliation":[]},{"given":"Jieren","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Xiangyan","family":"Tang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,26]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Meng, L., et al.: FedEAN: entity-aware adversarial negative sampling for federated knowledge graph reasoning. IEEE Transactions on Knowledge and Data Engineering (2024)","DOI":"10.1109\/TKDE.2024.3464516"},{"key":"3_CR2","first-page":"18839","volume":"34","author":"H Xie","year":"2021","unstructured":"Xie, H., Ma, J., Xiong, L., Yang, C.: Federated graph classification over non-IID graphs. Adv. Neural. Inf. Process. Syst. 34, 18839\u201318852 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Wang, B., Li, A., Pang, M., Li, H., Chen, Y.: GraphFL: A federated learning framework for semi-supervised node classification on graphs. In: 2022 IEEE International Conference on Data Mining, pp. 498\u2013507 (2022)","DOI":"10.1109\/ICDM54844.2022.00060"},{"issue":"18","key":"3_CR4","doi-asserted-by":"publisher","first-page":"18870","DOI":"10.1609\/aaai.v39i18.34077","volume":"39","author":"J Liu","year":"2025","unstructured":"Liu, J., Cheng, J., Han, R., Tu, W., Wang, J., Peng, X.: Federated graph-level clustering network. Proceedings of the AAAI Conference on Artificial Intelligence. 39(18), 18870\u201318878 (2025)","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence."},{"key":"3_CR5","first-page":"6671","volume":"34","author":"K Zhang","year":"2021","unstructured":"Zhang, K., Yang, C., Li, X., Sun, L., Yiu, S.M.: Subgraph federated learning with missing neighbor generation. Adv. Neural. Inf. Process. Syst. 34, 6671\u20136682 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"6","key":"3_CR6","doi-asserted-by":"publisher","first-page":"1848","DOI":"10.1109\/TCAD.2023.3346274","volume":"43","author":"J Cao","year":"2023","unstructured":"Cao, J., et al.: FedSTAR: efficient federated learning on heterogeneous communication networks. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 43(6), 1848\u20131861 (2023)","journal-title":"IEEE Trans. Comput. Aided Des. Integr. Circuits Syst."},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Liang, K., et al.: A survey of knowledge graph reasoning on graph types: static, dynamic, and multi-modal. IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)","DOI":"10.1109\/TPAMI.2024.3417451"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Tu, W., et al.: WAGE: weight-sharing attribute-missing graph autoencoder. IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1\u201318 (2025)","DOI":"10.1109\/TPAMI.2025.3554053"},{"key":"3_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110484","volume":"154","author":"L Song","year":"2024","unstructured":"Song, L., Tu, W., Zhou, S., Zhu, E.: GANN: graph alignment neural network for semisupervised learning. Pattern Recogn. 154, 110484 (2024)","journal-title":"Pattern Recogn."},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Liang, K., et al.: MGKsite: multi-modal knowledge-driven site selection via intra and inter-modal graph fusion. IEEE Transactions on Multimedia (2024)","DOI":"10.1109\/TMM.2024.3521742"},{"key":"3_CR11","doi-asserted-by":"publisher","first-page":"8733","DOI":"10.1609\/aaai.v38i8.28719","volume":"38","author":"K Liang","year":"2024","unstructured":"Liang, K., et al.: Hawkes-enhanced spatial-temporal hypergraph contrastive learning based on criminal correlations. Proceedings of the AAAI Conference on Artificial Intelligence 38, 8733\u20138741 (2024)","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Yu, H., et al.: GZOO: black-box node injection attack on graph neural networks via zeroth-order optimization. IEEE Transactions on Knowledge and Data Engineering (2024)","DOI":"10.1109\/TKDE.2024.3483274"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Liu, S., et al.: Learn from view correlation: an anchor enhancement strategy for multi-view clustering. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 26151--26161 (2024)","DOI":"10.1109\/CVPR52733.2024.02471"},{"issue":"1","key":"3_CR14","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1109\/TKDE.2023.3282989","volume":"36","author":"K Liang","year":"2023","unstructured":"Liang, K., et al.: Knowledge graph contrastive learning based on relation-symmetrical structure. IEEE Trans. Knowl. Data Eng. 36(1), 226\u2013238 (2023)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Liang, K., et al.: Learn from relational correlations and periodic events for temporal knowledge graph reasoning. Proceedings of the 46th International ACM SIGIR Conference, pp. 1559\u20131568 (2023)","DOI":"10.1145\/3539618.3591711"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Liang, K., et al.: Simple yet effective: structure guided pre-trained transformer for multi-modal knowledge graph reasoning. Proceedings of the 32nd ACM International Conference on Multimedia, pp. 1554\u20131563 (2024)","DOI":"10.1145\/3664647.3681112"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Guan, R., et al.: Spatial-spectral graph contrastive clustering with hard sample mining for hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, pp. 1\u201316 (2024)","DOI":"10.1109\/TGRS.2024.3464648"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Wang, S., Liu, X., Liu, S., Tu, W., Zhu, E.: Scalable and structural multi-view graph clustering with adaptive anchor fusion. IEEE Transactions on Image Processing (2024)","DOI":"10.1109\/TIP.2024.3444320"},{"issue":"16","key":"3_CR19","doi-asserted-by":"publisher","first-page":"9551","DOI":"10.1007\/s00521-024-09575-4","volume":"36","author":"J Cai","year":"2024","unstructured":"Cai, J., Han, Y., Guo, W., Fan, J.: Deep graph-level clustering using pseudo-label-guided mutual information maximization network. Neural Comput. Appl. 36(16), 9551\u20139566 (2024)","journal-title":"Neural Comput. Appl."},{"key":"3_CR20","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282 (2017)"},{"key":"3_CR21","first-page":"429","volume":"2","author":"T Li","year":"2020","unstructured":"Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. Proceedings of Machine Learning and Systems 2, 429\u2013450 (2020)","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"3_CR22","unstructured":"Arivazhagan, M.G., Aggarwal, V., Singh, A.K., Choudhary, S.: Federated Learning with Personalization Layers. arXiv preprint arXiv:1912.00818 (2019)"},{"key":"3_CR23","doi-asserted-by":"publisher","first-page":"9978","DOI":"10.1609\/aaai.v35i11.17198","volume":"35","author":"W Tu","year":"2021","unstructured":"Tu, W., et al.: Deep fusion clustering network. Proceedings of the AAAI Conference on Artificial Intelligence 35, 9978\u20139987 (2021)","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Tan, Y., Liu, Y., Long, G., Jiang, J., Lu, Q., Zhang, C.: Federated learning on non-IID graphs via structural knowledge sharing. Proceedings of the AAAI Conference on Artificial Intelligence, pp. 9953\u20139961 (2023)","DOI":"10.1609\/aaai.v37i8.26187"},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"Cai, J., Zhang, Y., Fan, J., Ng, S.K.: LG-FGAD: an effective federated graph anomaly detection framework. Proceedings of the International Joint Conference on Artificial Intelligence (2024)","DOI":"10.24963\/ijcai.2024\/416"},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Cai, J., Zhang, Y., Lu, Z., Guo, W., Ng, S.K.: Towards effective federated graph anomaly detection via self-boosted knowledge distillation. Proceedings of the 32nd ACM International Conference on Multimedia, pp. 5537\u20135546 (2024)","DOI":"10.1145\/3664647.3681415"},{"key":"3_CR27","doi-asserted-by":"crossref","unstructured":"Liu, M., et al.: TMAC: temporal multi-modal graph learning for acoustic event classification. Proceedings of the 31st ACM International Conference on Multimedia, pp. 3365\u20133374 (2023)","DOI":"10.1145\/3581783.3611853"},{"key":"3_CR28","doi-asserted-by":"crossref","unstructured":"Tu, W., et al.: Attribute-missing graph clustering network. Proceedings of the AAAI Conference on Artificial Intelligence, pp. 15392\u201315401 (2024)","DOI":"10.1609\/aaai.v38i14.29464"},{"key":"3_CR29","doi-asserted-by":"crossref","unstructured":"Tu, W., et al.: Deep fusion clustering network. Proceedings of the AAAI Conference on Artificial Intelligence, pp. 9978\u20139987 (2021)","DOI":"10.1609\/aaai.v35i11.17198"},{"key":"3_CR30","doi-asserted-by":"crossref","unstructured":"Tu, W., et al.: Initializing then refining: a simple graph attribute imputation network. Proceedings of the International Joint Conference on Artificial Intelligence, pp. 3494\u20133500 (2022)","DOI":"10.24963\/ijcai.2022\/485"},{"issue":"3","key":"3_CR31","doi-asserted-by":"publisher","first-page":"3244","DOI":"10.1109\/TNNLS.2024.3349850","volume":"36","author":"W Tu","year":"2025","unstructured":"Tu, W., Xiao, B., Liu, X., Zhou, S., Cai, Z., Cheng, J.: Revisiting initializing then refining: an incomplete and missing graph imputation network. IEEE Transactions on Neural Networks and Learning Systems 36(3), 3244\u20133257 (2025)","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"8","key":"3_CR32","doi-asserted-by":"publisher","first-page":"9552","DOI":"10.1109\/TPAMI.2023.3253211","volume":"45","author":"J Liu","year":"2023","unstructured":"Liu, J., Liu, X., Yang, Y., Liao, Q., Xia, Y.: Contrastive multi-view kernel learning. IEEE Trans. Pattern Anal. Mach. Intell. 45(8), 9552\u20139566 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"11","key":"3_CR33","doi-asserted-by":"publisher","first-page":"16748","DOI":"10.1109\/TNNLS.2023.3297607","volume":"35","author":"W Tu","year":"2024","unstructured":"Tu, W., Zhou, S., Liu, X., Ge, C., Cai, Z., Liu, Y.: Hierarchically contrastive hard sample mining for graph self-supervised pre-training. IEEE Transactions on Neural Networks and Learning Systems 35(11), 16748\u201316761 (2024)","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"10","key":"3_CR34","doi-asserted-by":"publisher","first-page":"5340","DOI":"10.1109\/TKDE.2023.3335222","volume":"36","author":"W Tu","year":"2024","unstructured":"Tu, W., et al.: RARE: robust masked graph autoencoder. IEEE Trans. Knowl. Data Eng. 36(10), 5340\u20135353 (2024)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"3_CR35","unstructured":"Liu, M., et al.: Deep temporal graph clustering. The 12th International Conference on Learning Representations (2024)"},{"key":"3_CR36","doi-asserted-by":"crossref","unstructured":"Liu, M., et al.: Self-supervised temporal graph learning with temporal and structural intensity alignment. IEEE Transactions on Neural Networks and Learning Systems (2024)","DOI":"10.1109\/TNNLS.2024.3386168"}],"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\/978-981-96-9921-6_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T22:49:27Z","timestamp":1757285367000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-9921-6_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819699209","9789819699216"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-9921-6_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"26 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"}}]}}