{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T12:05:16Z","timestamp":1784203516398,"version":"3.55.0"},"reference-count":68,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neural Networks"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.neunet.2026.109009","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T07:04:52Z","timestamp":1776323092000},"page":"109009","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["HC-GLAD: Dual hyperbolic contrastive learning for unsupervised graph-level anomaly detection"],"prefix":"10.1016","volume":"202","author":[{"given":"Yali","family":"Fu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2228-3696","authenticated-orcid":false,"given":"Jindong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianli","family":"Xing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Irwin","family":"King","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neunet.2026.109009_bib0001","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevE.89.032811","article-title":"Topological implications of negative curvature for biological and social networks","volume":"89","author":"Albert","year":"2014","journal-title":"Physical Review E"},{"issue":"1","key":"10.1016\/j.neunet.2026.109009_bib0002","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3605776","article-title":"A survey on hypergraph representation learning","volume":"56","author":"Antelmi","year":"2023","journal-title":"ACM Computing Surveys"},{"key":"10.1016\/j.neunet.2026.109009_bib0003","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107637","article-title":"Hypergraph convolution and hypergraph attention","volume":"110","author":"Bai","year":"2021","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neunet.2026.109009_bib0004","series-title":"Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining","first-page":"16","article-title":"Efficient semi-streaming algorithms for local triangle counting in massive graphs","author":"Becchetti","year":"2008"},{"issue":"9","key":"10.1016\/j.neunet.2026.109009_bib0005","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1109\/TAC.2013.2254619","article-title":"Stochastic gradient descent on Riemannian manifolds","volume":"58","author":"Bonnabel","year":"2013","journal-title":"IEEE Transactions on Automatic Control"},{"key":"10.1016\/j.neunet.2026.109009_bib0006","series-title":"International conference on machine learning","first-page":"1045","article-title":"Latent variable modelling with hyperbolic normalizing flows","author":"Bose","year":"2020"},{"key":"10.1016\/j.neunet.2026.109009_bib0007","unstructured":"Bu, T., Wang, C., Ma, H., Zheng, H., Lu, X., & Wu, T. (2025). GGball: Graph generative model on poincar\\\u2019e ball. arXiv: 2506.07198."},{"key":"10.1016\/j.neunet.2026.109009_bib0008","article-title":"Hyperbolic graph convolutional neural networks","volume":"32","author":"Chami","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2026.109009_bib0009","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106115","article-title":"Graph global attention network with memory: A deep learning approach for fake news detection","volume":"172","author":"Chang","year":"2024","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109009_bib0010","unstructured":"Chen, F., Park, J., & Park, J. (2021). A molecular hyper-message passing network with functional group information. arXiv: 2106.01028."},{"issue":"14","key":"10.1016\/j.neunet.2026.109009_bib0011","doi-asserted-by":"crossref","DOI":"10.1063\/5.0193557","article-title":"Molecular hypergraph neural networks","volume":"160","author":"Chen","year":"2024","journal-title":"The Journal of Chemical Physics"},{"key":"10.1016\/j.neunet.2026.109009_bib0012","series-title":"Proceedings of the ACM on web conference 2025","first-page":"1261","article-title":"Kronecker generative models for power-law patterns in real-world hypergraphs","author":"Choe","year":"2025"},{"key":"10.1016\/j.neunet.2026.109009_bib0013","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"3558","article-title":"Hypergraph neural networks","volume":"vol. 33","author":"Feng","year":"2019"},{"key":"10.1016\/j.neunet.2026.109009_bib0014","series-title":"Proceedings of the 41st international conference on machine learning","first-page":"14102","article-title":"Hyperbolic geometric latent diffusion model for graph generation","author":"Fu","year":"2024"},{"key":"10.1016\/j.neunet.2026.109009_bib0015","series-title":"Proceedings of the ACM web conference 2023","first-page":"460","article-title":"Hyperbolic geometric graph representation learning for hierarchy-imbalance node classification","author":"Fu","year":"2023"},{"issue":"3","key":"10.1016\/j.neunet.2026.109009_bib0016","doi-asserted-by":"crossref","first-page":"3181","DOI":"10.1109\/TPAMI.2022.3182052","article-title":"HGNN+: General hypergraph neural networks","volume":"45","author":"Gao","year":"2022","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"5","key":"10.1016\/j.neunet.2026.109009_bib0017","first-page":"2548","article-title":"Hypergraph learning: Methods and practices","volume":"44","author":"Gao","year":"2022","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.neunet.2026.109009_bib0018","series-title":"Hyperbolic groups","first-page":"75","author":"Gromov","year":"1987"},{"key":"10.1016\/j.neunet.2026.109009_bib0019","unstructured":"Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv: 1609.02907."},{"issue":"8","key":"10.1016\/j.neunet.2026.109009_bib0020","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3719002","article-title":"A survey on hypergraph mining: Patterns, tools, and generators","volume":"57","author":"Lee","year":"2025","journal-title":"ACM Computing Surveys"},{"key":"10.1016\/j.neunet.2026.109009_bib0021","unstructured":"Lee, G., Ko, J., & Shin, K. (2020). Hypergraph motifs: concepts, algorithms, and discoveries. arXiv: 2003.01853."},{"key":"10.1016\/j.neunet.2026.109009_bib0022","series-title":"Joint European conference on machine learning and knowledge discovery in databases","first-page":"185","article-title":"CVTGAD: Simplified transformer with cross-view attention for unsupervised graph-level anomaly detection","author":"Li","year":"2023"},{"key":"10.1016\/j.neunet.2026.109009_bib0023","series-title":"2008 Eighth IEEE international conference on data mining","first-page":"413","article-title":"Isolation forest","author":"Liu","year":"2008"},{"key":"10.1016\/j.neunet.2026.109009_bib0024","series-title":"Proceedings of the sixteenth ACM international conference on web search and data mining","first-page":"339","article-title":"Good-d: On unsupervised graph out-of-distribution detection","author":"Liu","year":"2023"},{"issue":"6","key":"10.1016\/j.neunet.2026.109009_bib0025","first-page":"5879","article-title":"Graph self-supervised learning: A survey","volume":"35","author":"Liu","year":"2022","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.neunet.2026.109009_bib0026","series-title":"Proceedings of the AAAI conference on artificial intelligence","article-title":"Beyond smoothing: Unsupervised graph representation learning with edge heterophily discriminating","author":"Liu","year":"2023"},{"key":"10.1016\/j.neunet.2026.109009_bib0027","series-title":"Advances in neural information processing systems","article-title":"Towards self-interpretable graph-level anomaly detection","volume":"36","author":"Liu","year":"2024"},{"issue":"1","key":"10.1016\/j.neunet.2026.109009_bib0028","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-022-22086-3","article-title":"Deep graph level anomaly detection with contrastive learning","volume":"12","author":"Luo","year":"2022","journal-title":"Scientific Reports"},{"key":"10.1016\/j.neunet.2026.109009_bib0029","series-title":"Proceedings of the fifteenth ACM international conference on web search and data mining","first-page":"704","article-title":"Deep graph-level anomaly detection by glocal knowledge distillation","author":"Ma","year":"2022"},{"issue":"12","key":"10.1016\/j.neunet.2026.109009_bib0030","doi-asserted-by":"crossref","first-page":"12012","DOI":"10.1109\/TKDE.2021.3118815","article-title":"A comprehensive survey on graph anomaly detection with deep learning","volume":"35","author":"Ma","year":"2021","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"12","key":"10.1016\/j.neunet.2026.109009_bib0031","first-page":"139","article-title":"One-class SVMs for document classification","volume":"2","author":"Manevitz","year":"2001","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.neunet.2026.109009_bib0032","unstructured":"Morris, C., Kriege, N. M., Bause, F., Kersting, K., Mutzel, P., & Neumann, M. (2020). TUdataset: A collection of benchmark datasets for learning with graphs. arXiv: 2007.08663."},{"key":"10.1016\/j.neunet.2026.109009_bib0033","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/s10994-015-5517-9","article-title":"Propagation kernels: Efficient graph kernels from propagated information","volume":"102","author":"Neumann","year":"2016","journal-title":"Machine Learning"},{"key":"10.1016\/j.neunet.2026.109009_bib0034","series-title":"International conference on machine learning","first-page":"3779","article-title":"Learning continuous hierarchies in the Lorentz model of hyperbolic geometry","author":"Nickel","year":"2018"},{"key":"10.1016\/j.neunet.2026.109009_bib0035","series-title":"Advances in neural information processing systems","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019"},{"key":"10.1016\/j.neunet.2026.109009_bib0036","series-title":"Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining","first-page":"1150","article-title":"GCC: Graph contrastive coding for graph neural network pre-training","author":"Qiu","year":"2020"},{"issue":"2","key":"10.1016\/j.neunet.2026.109009_bib0037","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevE.67.026112","article-title":"Hierarchical organization in complex networks","volume":"67","author":"Ravasz","year":"2003","journal-title":"Physical Review E"},{"issue":"9","key":"10.1016\/j.neunet.2026.109009_bib0038","first-page":"2539","article-title":"Weisfeiler-Lehman graph kernels","volume":"12","author":"Shervashidze","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.neunet.2026.109009_bib0039","series-title":"International conference on learning representations","article-title":"Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization","author":"Sun","year":"2020"},{"key":"10.1016\/j.neunet.2026.109009_bib0040","series-title":"Proceedings of the web conference 2021","first-page":"593","article-title":"Hgcf: Hyperbolic graph convolution networks for collaborative filtering","author":"Sun","year":"2021"},{"key":"10.1016\/j.neunet.2026.109009_bib0041","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106645","article-title":"GTC: GNN-transformer co-contrastive learning for self-supervised heterogeneous graph representation","volume":"181","author":"Sun","year":"2025","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109009_bib0042","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"9953","article-title":"Federated learning on non-iid graphs via structural knowledge sharing","volume":"vol. 37","author":"Tan","year":"2023"},{"key":"10.1016\/j.neunet.2026.109009_bib0043","unstructured":"Tifrea, A., B\u00e9cigneul, G., & Ganea, O.-E. (2018). Poincar\\\u2019e glove: Hyperbolic word embeddings. arXiv: 1810.06546."},{"key":"10.1016\/j.neunet.2026.109009_bib0044","series-title":"Proceedings of the 26th international conference on world wide web","first-page":"1451","article-title":"Scalable motif-aware graph clustering","author":"Tsourakakis","year":"2017"},{"issue":"11","key":"10.1016\/j.neunet.2026.109009_bib0045","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"Journal of Machine Learning Research"},{"issue":"3","key":"10.1016\/j.neunet.2026.109009_bib0046","first-page":"4","article-title":"Deep graph infomax","volume":"2","author":"Velickovic","year":"2019","journal-title":"ICLR (Poster)"},{"key":"10.1016\/j.neunet.2026.109009_bib0047","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"15537","article-title":"Goodat: Towards test-time graph out-of-distribution detection","volume":"vol. 38","author":"Wang","year":"2024"},{"key":"10.1016\/j.neunet.2026.109009_bib0048","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107169","article-title":"Graph anomaly detection based on hybrid node representation learning","volume":"185","author":"Wang","year":"2025","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109009_bib0049","series-title":"The thirteenth international conference on learning representations","article-title":"Unifying unsupervised graph-level anomaly detection and out-of-distribution detection: A benchmark","author":"Wang","year":"2025"},{"key":"10.1016\/j.neunet.2026.109009_bib0050","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.neunet.2023.10.019","article-title":"Beyond the individual: An improved telecom fraud detection approach based on latent synergy graph learning","volume":"169","author":"Wu","year":"2024","journal-title":"Neural Networks"},{"issue":"4","key":"10.1016\/j.neunet.2026.109009_bib0051","doi-asserted-by":"crossref","first-page":"4216","DOI":"10.1109\/TKDE.2021.3131584","article-title":"Self-supervised learning on graphs: Contrastive, generative, or predictive","volume":"35","author":"Wu","year":"2021","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.neunet.2026.109009_bib0052","series-title":"Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval","first-page":"70","article-title":"Hypergraph contrastive collaborative filtering","author":"Xia","year":"2022"},{"key":"10.1016\/j.neunet.2026.109009_bib0053","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106804","article-title":"Gmni: Achieve good data augmentation in unsupervised graph contrastive learning","volume":"181","author":"Xiong","year":"2025","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109009_bib0054","unstructured":"Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2018). How powerful are graph neural networks?arXiv: 1810.00826."},{"key":"10.1016\/j.neunet.2026.109009_bib0055","first-page":"1511","article-title":"HyperGCN: A new method for training graph convolutional networks on hypergraphs","volume":"32","author":"Yadati","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2026.109009_bib0056","series-title":"Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining","first-page":"2212","article-title":"Hicf: Hyperbolic informative collaborative filtering","author":"Yang","year":"2022"},{"key":"10.1016\/j.neunet.2026.109009_bib0057","unstructured":"Yang, M., Zhou, M., Li, Z., Liu, J., Pan, L., Xiong, H., & King, I. (2022b). Hyperbolic graph neural networks: A review of methods and applications. arXiv: 2202.13852."},{"key":"10.1016\/j.neunet.2026.109009_bib0058","series-title":"Proceedings of the ACM web conference 2022","first-page":"2462","article-title":"HRCF: Enhancing collaborative filtering via hyperbolic geometric regularization","author":"Yang","year":"2022"},{"issue":"11","key":"10.1016\/j.neunet.2026.109009_bib0059","doi-asserted-by":"crossref","first-page":"11489","DOI":"10.1109\/TKDE.2022.3232398","article-title":"Hyperbolic temporal network embedding","volume":"35","author":"Yang","year":"2023","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.neunet.2026.109009_bib0060","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume":"33","author":"You","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2026.109009_bib0061","series-title":"Proceedings of the web conference 2021","first-page":"413","article-title":"Self-supervised multi-channel hypergraph convolutional network for social recommendation","author":"Yu","year":"2021"},{"key":"10.1016\/j.neunet.2026.109009_bib0062","series-title":"International conference on database systems for advanced applications","first-page":"415","article-title":"Tuaf: Triple-unit-based graph-level anomaly detection with adaptive fusion readout","author":"Yu","year":"2023"},{"key":"10.1016\/j.neunet.2026.109009_bib0063","series-title":"Proceedings of the 30th ACM international conference on information & knowledge management","first-page":"2557","article-title":"Double-scale self-supervised hypergraph learning for group recommendation","author":"Zhang","year":"2021"},{"issue":"6","key":"10.1016\/j.neunet.2026.109009_bib0064","first-page":"1690","article-title":"Hyperbolic graph attention network","volume":"8","author":"Zhang","year":"2021","journal-title":"IEEE Transactions on Big Data"},{"key":"10.1016\/j.neunet.2026.109009_bib0065","series-title":"Proceedings of the web conference 2021","first-page":"1249","article-title":"Lorentzian graph convolutional networks","author":"Zhang","year":"2021"},{"key":"10.1016\/j.neunet.2026.109009_bib0066","series-title":"Proceedings of the AAAI conference on artificial intelligence","article-title":"Ranking users in social networks with higher-order structures","volume":"vol. 32","author":"Zhao","year":"2018"},{"issue":"3","key":"10.1016\/j.neunet.2026.109009_bib0067","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1089\/big.2021.0069","article-title":"On using classification datasets to evaluate graph outlier detection: Peculiar observations and new insights","volume":"11","author":"Zhao","year":"2021","journal-title":"Big Data"},{"key":"10.1016\/j.neunet.2026.109009_bib0068","series-title":"Proceedings of the web conference 2021","first-page":"2069","article-title":"Graph contrastive learning with adaptive augmentation","author":"Zhu","year":"2021"}],"container-title":["Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026004703?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026004703?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T11:22:44Z","timestamp":1784200964000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0893608026004703"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":68,"alternative-id":["S0893608026004703"],"URL":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109009","relation":{},"ISSN":["0893-6080"],"issn-type":[{"value":"0893-6080","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"HC-GLAD: Dual hyperbolic contrastive learning for unsupervised graph-level anomaly detection","name":"articletitle","label":"Article Title"},{"value":"Neural Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109009","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"109009"}}