{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T15:56:54Z","timestamp":1767801414906,"version":"3.49.0"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032059611","type":"print"},{"value":"9783032059628","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:00:00Z","timestamp":1759536000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:00:00Z","timestamp":1759536000000},"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\/978-3-032-05962-8_11","type":"book-chapter","created":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T19:38:45Z","timestamp":1759520325000},"page":"176-192","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HCT: A Hierarchical Contrastive Learning Framework for\u00a0Transferable Graph Anomaly Detection"],"prefix":"10.1007","author":[{"given":"Jiawei","family":"Ye","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinlin","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sicheng","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,4]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Duan, M., Zheng, T., Gao, Y., Wang, G., Feng, Z., Wang, X.: DGA-GNN: dynamic grouping aggregation GNN for fraud detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a038, pp. 11820\u201311828 (2024)","DOI":"10.1609\/aaai.v38i10.29067"},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"McAuley, J.J., Leskovec, J.: From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 897\u2013908 (2013)","DOI":"10.1145\/2488388.2488466"},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Bian, T., et al.: Rumor detection on social media with bi-directional graph convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a034, pp. 549\u2013556 (2020)","DOI":"10.1609\/aaai.v34i01.5393"},{"key":"11_CR4","first-page":"29628","volume":"36","author":"J Tang","year":"2023","unstructured":"Tang, J., Hua, F., Gao, Z., Zhao, P., Li, J.: Gadbench: revisiting and benchmarking supervised graph anomaly detection. Adv. Neural. Inf. Process. Syst. 36, 29628\u201329653 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"11_CR5","unstructured":"Liu, Y., Li, S., Zheng, Y., Chen, Q., Zhang, C., Pan, S.: ARC: a generalist graph anomaly detector with in-context learning. In: Advances in Neural Information Processing Systems (2024)"},{"key":"11_CR6","first-page":"7793","volume":"33","author":"J Zhu","year":"2020","unstructured":"Zhu, J., Yan, Y., Zhao, L., Heimann, M., Akoglu, L., Koutra, D.: Beyond homophily in graph neural networks: current limitations and effective designs. Adv. Neural. Inf. Process. Syst. 33, 7793\u20137804 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Gao, Y., Wang, X., He, X., Liu, Z., Feng, H., Zhang, Y.: Addressing heterophily in graph anomaly detection: a perspective of graph spectrum. In: Proceedings of the ACM Web Conference 2023, pp. 1528\u20131538 (2023)","DOI":"10.1145\/3543507.3583268"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Chai, Z., et al.: Can abnormality be detected by graph neural networks? In: IJCAI, pp. 1945\u20131951 (2022)","DOI":"10.24963\/ijcai.2022\/270"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Ding, K., Li, J., Bhanushali, R., Liu, H.: Deep anomaly detection on attributed networks. In: Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 594\u2013602. SIAM (2019)","DOI":"10.1137\/1.9781611975673.67"},{"issue":"12","key":"11_CR10","doi-asserted-by":"publisher","first-page":"12220","DOI":"10.1109\/TKDE.2021.3119326","volume":"35","author":"Y Zheng","year":"2021","unstructured":"Zheng, Y., Jin, M., Liu, Y., Chi, L., Phan, K.T., Chen, Y.P.P.: Generative and contrastive self-supervised learning for graph anomaly detection. IEEE Trans. Knowl. Data Eng. 35(12), 12220\u201312233 (2021)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"6","key":"11_CR11","doi-asserted-by":"publisher","first-page":"2378","DOI":"10.1109\/TNNLS.2021.3068344","volume":"33","author":"Y Liu","year":"2021","unstructured":"Liu, Y., Li, Z., Pan, S., Gong, C., Zhou, C., Karypis, G.: Anomaly detection on attributed networks via contrastive self-supervised learning. IEEE Trans. Neural Netw. Learn. Syst. 33(6), 2378\u20132392 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Zhao, H., Chen, A., Sun, X., Cheng, H., Li, J.: All in one and one for all: a simple yet effective method towards cross-domain graph pretraining. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 4443\u20134454 (2024)","DOI":"10.1145\/3637528.3671913"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Yang, Z.R., Han, J., Wang, C.D., Liu, H.: GraphLoRA: structure-aware contrastive low-rank adaptation for cross-graph transfer learning. arXiv preprint arXiv:2409.16670 (2024)","DOI":"10.1145\/3690624.3709186"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Gui, A., Ye, J., Xiao, H.: G-Adapter: towards structure-aware parameter-efficient transfer learning for graph transformer networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a038, pp. 12226\u201312234 (2024)","DOI":"10.1609\/aaai.v38i11.29112"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Qiu, J., et al.: GCC: graph contrastive coding for graph neural network pre-training. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1150\u20131160 (2020)","DOI":"10.1145\/3394486.3403168"},{"key":"11_CR16","first-page":"49490","volume":"36","author":"H Qiao","year":"2023","unstructured":"Qiao, H., Pang, G.: Truncated affinity maximization: one-class homophily modeling for graph anomaly detection. Adv. Neural. Inf. Process. Syst. 36, 49490\u201349512 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Wang, Y., Shi, H., Zhang, Z., Jiao, D., Tang, S.: GraphControl: adding conditional control to universal graph pre-trained models for graph domain transfer learning. In: Proceedings of the ACM Web Conference 2024, pp. 539\u2013550 (2024)","DOI":"10.1145\/3589334.3645439"},{"issue":"2","key":"11_CR18","first-page":"3","volume":"1","author":"EJ Hu","year":"2022","unstructured":"Hu, E.J., et al.: LoRA: low-rank adaptation of large language models. ICLR 1(2), 3 (2022)","journal-title":"ICLR"},{"issue":"12","key":"11_CR19","doi-asserted-by":"publisher","first-page":"12012","DOI":"10.1109\/TKDE.2021.3118815","volume":"35","author":"X Ma","year":"2021","unstructured":"Ma, X., et al.: A comprehensive survey on graph anomaly detection with deep learning. IEEE Trans. Knowl. Data Eng. 35(12), 12012\u201312038 (2021)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"11_CR20","unstructured":"Tang, J., Li, J., Gao, Z., Li, J.: Rethinking graph neural networks for anomaly detection. In: International Conference on Machine Learning, pp. 21076\u201321089. PMLR (2022)"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Huang, T., Pei, Y., Menkovski, V., Pechenizkiy, M.: Hop-count based self-supervised anomaly detection on attributed networks. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 225\u2013241. Springer (2022)","DOI":"10.1007\/978-3-031-26387-3_14"},{"key":"11_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2021.108310","volume":"190","author":"H Hafidi","year":"2022","unstructured":"Hafidi, H., Ghogho, M., Ciblat, P., Swami, A.: Negative sampling strategies for contrastive self-supervised learning of graph representations. Signal Process. 190, 108310 (2022)","journal-title":"Signal Process."},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Duan, J., et al.: Graph anomaly detection via multi-scale contrastive learning networks with augmented view. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a037, pp. 7459\u20137467 (2023)","DOI":"10.1609\/aaai.v37i6.25907"},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Li, S., Han, X., Bai, J.: AdapterGNN: parameter-efficient fine-tuning improves generalization in GNNs. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a038, pp. 13600\u201313608 (2024)","DOI":"10.1609\/aaai.v38i12.29264"},{"key":"11_CR25","unstructured":"Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., Weinberger, K.: Simplifying graph convolutional networks. In: International Conference on Machine Learning, pp. 6861\u20136871. PMLR (2019)"},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Gao, Y., Wang, X., He, X., Liu, Z., Feng, H., Zhang, Y.: Alleviating structural distribution shift in graph anomaly detection. In: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, pp. 357\u2013365 (2023)","DOI":"10.1145\/3539597.3570377"},{"key":"11_CR27","doi-asserted-by":"crossref","unstructured":"Jin, M., Liu, Y., Zheng, Y., Chi, L., Li, Y.F., Pan, S.: ANEMONE: graph anomaly detection with multi-scale contrastive learning. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 3122\u20133126","DOI":"10.1145\/3459637.3482057"},{"key":"11_CR28","unstructured":"Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)"},{"key":"11_CR29","unstructured":"Platonov, O., Kuznedelev, D., Diskin, M., Babenko, A., Prokhorenkova, L.: A critical look at the evaluation of GNNs under heterophily: are we really making progress? arXiv preprint arXiv:2302.11640 (2023)"},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Kumar, S., Zhang, X., Leskovec, J.: Predicting dynamic embedding trajectory in temporal interaction networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1269\u20131278 (2019)","DOI":"10.1145\/3292500.3330895"},{"key":"11_CR31","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018)"},{"key":"11_CR32","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. Adv. Neural Inf. Process. Syst. 30 (2017)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05962-8_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T13:06:52Z","timestamp":1767791212000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05962-8_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,4]]},"ISBN":["9783032059611","9783032059628"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05962-8_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,4]]},"assertion":[{"value":"4 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Porto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","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 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}