Abstract Graph clustering aims to partition graph nodes into different categories in an unsupervised manner, facilitating the discovery of hidden patterns, community structures and organizational relationships within complex systems. Existing methods construct different self-supervised information through various learning paradigms to guide graph representation learning and promote clustering. Therefore, the learning paradigm is the key to clustering algorithms. However, few existing reviews discuss graph clustering from the perspective of different learning paradigms. In this paper, the research progress on graph clustering based on different learning paradigms is summarized. Clustering methods are classified into reconstructive graph clustering, contrastive graph clustering, adversarial graph clustering and hybrid graph clustering. Considering the research scope and clustering effect, reconstructive graph clustering and contrastive graph clustering are discussed in detail. Graph clustering results on single-relation and multi-relation datasets are compared. The results show that contrastive graph clustering performs better on single-relation datasets, while reconstructive graph clustering is more effective on multi-relation datasets. Finally, the challenges faced in the graph clustering field are summarized, and future research directions are pointed out as well. The applications of deep graph clustering across various domains are additionally introduced.
Fund:National Natural Science Foundation of China(No.62006211)
Corresponding Authors:NIU Changyong, Ph.D., associate professor. His research interests include large-scale data management and analysis on cloud platforms, machine learning with a focus on deep learning algorithms and applications, and mobile terminal system development.
About author:: ZHOU Lijuan, Ph.D., associate profe-ssor. Her research interests include computer vision, machine learning and multimodal processing. WU Mengqi, Master student. Her research interests include data mining, attributed graph clustering and contrastive learning. LI Xinran, Master student. His research interests include multimodal recommendation and machine learning.
ZHOU Lijuan,WU Mengqi,LI Xinran等. A Survey of Deep Graph Clustering Methods Based on Different Learning Paradigms[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(3): 233-251.
[1] ADJEISAH M, ZHU X Z, XU H Y, et al. Graph Contrastive Multi-view Learning: A Pre-training Framework for Graph Classification. Knowledge-Based Systems, 2024, 299. DOI: 10.1016/j.knosys.2024.112112. [2] CHEN J L, MA T F, XU L W.Revolutionizing Graph Classification Generalization with an Adaptive Causality-Enhanced Framework. Neurocomputing, 2025, 631. DOI: 10.1016/j.neucom.2025.129716. [3] KIPF T N, WELLING M.Variational Graph Auto-Encoders[C/OL]. [2024-11-12]. https://arxiv.org/pdf/1611.07308. [4] YANG X H, WANG Y Q, LIU Y, et al. Mixed Graph Contrastive Network for Semi-Supervised Node Classification. ACM Transactions on Knowledge Discovery from Data, 2024. DOI: 10.1145/3641549. [5] LIANG K, MENG L Y, LIU M, et al. Learn from Relational Correlations and Periodic Events for Temporal Knowledge Graph Reaso-ning // Proc of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2023: 1559-1568. [6] LIANG K, MENG L Y, ZHOU S H, et al. Message Intercommunication for Inductive Relation Reasoning[C/OL].[2024-11-12]. https://arxiv.org/pdf/2305.14074. [7] LIANG K, TAN J, ZENG D R, et al. ABSLearn: A GNN-Based Framework for Aliasing and Buffer-Size Information Retrieval. Pa-ttern Analysis and Applications, 2023, 26: 1171-1189. [8] QUAN Y H, DING J T, GAO C, et al. Robust Preference-Guided Denoising for Graph Based Social Recommendation // Proc of the ACM Web Conference. New York, USA: ACM, 2023: 1097-1108. [9] YI K, ZHOU B X, SHEN Y Q, et al. Graph Denoising Diffusion for Inverse Protein Folding // Proc of the 37th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2023: 10238-10257. [10] LECUN Y, BENGIO Y, HINTON G. Deep Learning. Nature, 2015, 521(7553): 436-444. [11] GHODSI S, SEYEDI S A, NTOUTSI E.Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering // Proc of the 28th Pacific-Asia Conference on Know-ledge Discovery and Data Mining. Berlin, Germany: Springer, 2024: 284-296. [12] FAN H Y, ZHANG F B, LI Z Y.Anomalydae: Dual Autoencoder for Anomaly Detection on Attributed Networks // Proc of the IEEE International Conference on Acoustics, Speech and Signal Proce-ssing. Washington, USA: IEEE, 2020: 5685-5689. [13] LI J H, FAN S H.GRNN: Graph-Retraining Neural Network for Semi-Supervised Node Classification. Algorithms, 2023, 16(3). DOI: 10.3390/a16030126. [14] SUN Y, YIN S L, LI H, et al. GPOGC: Gaussian Pigeon-Oriented Graph Clustering Algorithm for Social Networks Cluster. IEEE Access, 2019, 7: 99254-99262. [15] BANAGANAPALLI B, MANSOUR H, MOHAMMED A, et al. Ex-ploring Celiac Disease Candidate Pathways by Global Gene Expre-ssion Profiling and Gene Network Cluster Analysis. Scientific Reports, 2020, 10(1). DOI: 10.1038/s41598-020-73288-6. [16] LIN Z H, TIAN C X, HOU Y P, et al. Improving Graph Collaborative Filtering with Neighborhood-Enriched Contrastive Learning // Proc of the ACM Web Conference. New York, USA: ACM, 2022: 2320-2329. [17] 张衡,谭晓阳,金鑫.基于多视图聚类的自然图像边缘检测.模式识别与人工智能, 2016, 29(2): 163-170. (ZHANG H, TAN X Y, JIN X.Multi-view Clustering Based Natural Image Contour Detection. Pattern Recognition and Artificial Intelligence, 2016, 29(2): 163-170.) [18] 方宝富,张旭,王浩.联合深度图聚类与目标检测的像素级分割算法.模式识别与人工智能, 2022, 35(2): 130-140. (FANG B F, ZHANG X, WANG H.Pixel-Level Segmentation Algorithm Combining Depth Map Clustering and Object Detection. Pattern Recognition and Artificial Intelligence, 2022, 35(2): 130-140.) [19] 胡素婷,沈宗鑫,黄倩倩,等.基于概念分解的显隐空间协同多视图聚类算法.模式识别与人工智能, 2023, 36(2): 160-173. (HU S T, SHEN Z X, HUANG Q Q, et al. Concept Factorization-Based Collaborative Multi-view Clustering Algorithm in Visible and Latent Spaces. Pattern Recognition and Artificial Intelligence, 2023, 36(2): 160-173. ) [20] HARTIGAN J A, WONG M A.A K-means Clustering Algorithm. Journal of the Royal Statistical Society(Applied Statistics), 1979, 28(1): 100-108. [21] NG A Y, JORDAN M I, WEISS Y.On Spectral Clustering: Ana-lysis and an Algorithm // Proc of the 15th International Conference on Neural Information Processing Systems: Natural and Synthetic. Cambridge, USA: MIT Press, 2001: 849-856. [22] TIAN F, GAO B, CUI Q, et al. Learning Deep Representations for Graph Clustering. Proceedings of the AAAI Conference on Artificial Intelligence, 2014, 28(1): 1293-1299. [23] NG A.Sparse Autoencoder[C/OL]. [2024-11-12].https://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf. [24] CAO S S, LU W, XU Q K.Deep Neural Networks for Learning Graph Representations. Proceedings of the AAAI Conference on Artificial Intelligence, 2016, 30(1): 1145-1152. [25] SALEHI A, DAVULCU H.Graph Attention Auto-Encoders // Proc of the 32nd IEEE International Conference on Tools with Artificial Intelligence. Washington, USA: IEEE, 2020: 989-996. [26] WANG C, PAN S R, HU R Q, et al. Attributed Graph Clus-tering: A Deep Attentional Embedding Approach // Proc of the 28th International Joint Conference on Artificial Intelligence. San Francisco, USA: IJCAI, 2019: 3670-3676. [27] PARK J, LEE M, CHANG H J, et al. Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning // Proc of the IEEE/CVF International Conference on Computer Vision. Washington, USA: IEEE, 2019: 6518-6527. [28] ZHANG X T, LIU H, LI Q M, et al. Attributed Graph Clustering via Adaptive Graph Convolution // Proc of the 28th International Joint Conference on Artificial Intelligence. San Francisco, USA: IJCAI, 2019: 4327-4333. [29] CHENG J F, WANG Q Q, TAO Z Q, et al. Multi-view Attribute Graph Convolution Networks for Clustering // Proc of the 29th International Joint Conferences on Artificial Intelligence. San Francisco, USA: IJCAI, 2021: 2973-2979. [30] SUN D D, LI D S, DING Z L, et al. A2AE: Towards Adaptive Multi-view Graph Representation Learning via All-to-All Graph Autoencoder Architecture. Applied Soft Computing, 2022, 125. DOI: 10.1016/j.asoc.2022.109193. [31] HASSANI K, KHASAHMADI A H.Contrastive Multi-view Representation Learning on Graphs. Proceedings of Machine Learning Research, 2020, 119: 4116-4126. [32] GONG L, ZHOU S H, TU W X, et al. Attributed Graph Clus-tering with Dual Redundancy Reduction // Proc of the 31st International Joint Conference on Artificial Intelligence. San Francisco, USA: IJCAI, 2022: 3015-3021. [33] ZHU Y Q, XU Y C, YU F, et al. Graph Contrastive Learning with Adaptive Augmentation // Proc of the Web Conference. Washington, USA: IEEE, 2021: 2069-2080. [34] ZHOU S, XU H J, ZHENG Z N, et al. A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions. ACM Computing Surveys, 2024, 57(3). DOI: 10.1145/3689036. [35] FANG U, LI M, LI J X, et al. A Comprehensive Survey on Multi-view Clustering. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(12): 12350-12368. [36] 张荣国,曹俊辉,胡静,等.基于非负正交矩阵分解的多视图聚类图像分割算法.模式识别与人工智能, 2023, 36(6): 556-571. (ZHANG R G, CAO J H, HU J, et al. Non-negative Orthogonal Matrix Factorization Based Multi-view Clustering Image Segmentation Algorithm. Pattern Recognition and Artificial Intelligence, 2023, 36(6): 556-571.) [37] LIU Y, XIA J, ZHOU S H, et al. A Survey of Deep Graph Clus-tering: Taxonomy, Challenge, Application, and Open Resource [C/OL].[2024-11-12]. https://arxiv.org/abs/2211.12875v4. [38] WANG S P, YANG J B, YAO J, et al. An Overview of Advanced Deep Graph Node Clustering. IEEE Transactions on Computational Social Systems, 2024, 11(1): 1302-1314. [39] LUTOV A, KHAYATI M, CUDRÉ-MAUROUX P.Accuracy Eva-luation of Overlapping and Multi-resolution Clustering Algorithms on Large Datasets // Proc of the IEEE International Conference on Big Data and Smart Computing. Washington, USA: IEEE, 2019. DOI: 10.1109/BIGCOMP.2019.8679398. [40] MCDAID A F, GREENE D, HURLEY N.Normalized Mutual Information to Evaluate Overlapping Community Finding Algorithms[C/OL]. [2024-11-12]. https://arxiv.org/pdf/1110.2515. [41] YEUNG K Y, RUZZO W L.Details of the Adjusted Rand Index and Clustering Algorithms Supplement to the Paper "an Empirical Study on Principal Component Analysis for Clustering Gene Expre-ssion Data"(to Appear in Bioinformatics). Bioinformatics, 2001, 17(9): 763-774. [42] SEN P, NAMATA G, BILGIC M, et al. Collective Classification in Network Data. AI Magazine, 2008, 29(3): 93-106. [43] RIBEIRO L F R, SAVERESE P H P, FIGUEIREDO D R. struc2vec: Learning Node Representations from Structural Identity // Proc of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2017: 385-394. [44] WANG X, JI H Y, SHI C, et al. Heterogeneous Graph Attention Network // Proc of the World Wide Web Conference. New York, USA: ACM, 2019: 2022-2032. [45] VELICKOVIC P, CUCURULL G, CASANOVA A, et al. Graph Attention Networks[C/OL].[2024-11-12]. https://arxiv.org/pdf/1710.10903. [46] KIPF T N, WELLING M.Semi-Supervised Classification with Gra-ph Convolutional Networks[C/OL]. [2024-11-12]. https://arxiv.org/pdf/1609.02907. [47] SUN D D, LI D S, DING Z L, et al. Dual-Decoder Graph Autoenco-der for Unsupervised Graph Representation Learning. Knowledge-Based Systems, 2021, 234. DOI: 10.1016/j.knosys.2021.107564. [48] LI Q M, HAN Z C, WU X M.Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 2018, 32(1): 3538-3545. [49] HAJIVEISEH A, SEYEDI S A, TAB F A.Deep Asymmetric Nonnegative Matrix Factorization for Graph Clustering. Pattern Recognition, 2024, 148. DOI: 10.1016/j.patcog.2023.110179. [50] JANNESARI V, KESHVARI M, BERAHMAND K.A Novel Nonnegative Matrix Factorization-Based Model for Attributed Graph Clustering by Incorporating Complementary Information. Expert Systems with Applications, 2024, 242. DOI: 10.1016/j.eswa.2023.122799. [51] BERAHMAND K, MOHAMMADI M, SHEIKHPOUR R, et al. WSN-MF: Weighted Symmetric Nonnegative Matrix Factorization for Attributed Graph Clustering. Neurocomputing, 2024, 566. DOI: 10.1016/j.neucom.2023.127041. [52] TU W X, GUAN R X, ZHOU S, et al. Attribute-Missing Graph Clustering Network. Proceedings of the AAAI Conference on Artificial Intelligence, 2024, 38(14): 15392-15401. [53] WANG P F, WU D Q, CHEN C, et al. Deep Adaptive Graph Clustering via Von Mises-Fisher Distributions. ACM Transactions on the Web, 2024, 18(2). DOI: 10.1145/358052. [54] ZHU P F, WANG Q, WANG Y,et al. Every Node Is Different: Dynamically Fusing Self-Supervised Tasks for Attributed Graph Clustering. Proceedings of the AAAI Conference on Artificial Inte-lligence, 2024, 38(15): 17184-17192. [55] QU M, TANG J, SHANG J B, et al. An Attention-Based Collaboration Framework for Multi-view Network Representation Learning // Proc of the ACM Conference on Information and Knowledge Management. New York, USA: ACM, 2017: 1767-1776. [56] LIU J L, WANG C, GAO J, et al. Multi-view Clustering via Joint Nonnegative Matrix Factorization // Proc of the SIAM International Conference on Data Mining. Philadelphia, USA: SIAM, 2013: 252-260. [57] WANG Y, WU L, LIN X M, et al. Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(10): 4833-4843. [58] LI Y Q, NIE F P, HUANG H, et al. Large-Scale Multi-view Spectral Clustering via Bipartite Graph. Proceedings of the AAAI Conference on Artificial Intelligence, 2015, 29(1): 2750-2756. [59] FAN S H, WANG X W, SHI C, et al. One2multi Graph Autoencoder for Multi-view Graph Clustering // Proc of the Web Confe-rence. New York, USA: ACM, 2020: 3070-3076. [60] XIA W, WANG Q Q, GAO Q X, et al. Self-Supervised Graph Convolutional Network for Multi-view Clustering. IEEE Transactions on Multimedia, 2022, 24: 3182-3192. [61] ZHOU L J, GUO Y W, ZHANG Z H.Multi-view Attributed Graph Clustering Based on Graph Diffusion Convolution with Adaptive Fusion. Expert Systems with Applications, 2025, 260. DOI: 10.1016/j.eswa.2024.125286. [62] LIN Z P, KANG Z.Graph Filter-Based Multi-view Attributed Gra-ph Clustering // Proc of the 30th International Joint Conferences on Artificial Intelligence. San Francisco, USA: IJCAI, 2021: 2723-2729. [63] LIU J, CAO F Y, JING X C, et al. Deep Multi-view Graph Clustering Network with Weighting Mechanism and Collaborative Training. Expert Systems with Applications, 2024, 236. DOI: 10.1016/j.eswa.2023.121298. [64] CHEN J P, LING Y W, XU J, et al. Variational Graph Generator for Multiview Graph Clustering. IEEE Transactions on Neural Networks and Learning Systems, 2025. DOI: 10.1109/TNNLS.2024.3524205. [65] WEN Z C, LING Y W, REN Y Z, et al. Homophily-Related: Adaptive Hybrid Graph Filter for Multi-view Graph Clustering. Proceedings of the AAAI Conference on Artificial Intelligence, 2024, 38(14): 15841-15849. [66] WANG H N, ZHANG J Y, ZHU Q, et al. Augmentation-Free Gra-ph Contrastive Learning with Performance Guarantee[C/OL].[2024-11-12]. https://arxiv.org/pdf/2204.04874. [67] AHMADI M, SAFAYANI M, MIRZAEI A.Deep Graph Clustering via Mutual Information Maximization and Mixture Model. Know-ledge and Information Systems, 2024, 66: 4549-4572. [68] LEE N, LEE J, PARK C.Augmentation-Free Self-Supervised Lear-ning on Graphs. Proceedings of the AAAI Conference on Artificial Intelligence, 2022, 36(7): 7372-7380. [69] YANG H R, CHEN H X, PAN S R, et al. Dual Space Graph Con-trastive Learning // Proc of the ACM Web Conference. New York, USA: ACM, 2022: 1238-1247. [70] KULATILLEKE G K, PORTMANN M, CHANDRA S S.SCGC: Self-Supervised Contrastive Graph Clustering. Neurocomputing, 2025, 611. DOI: 10.1016/j.neucom.2024.128629. [71] LIU Y, YANG X H, ZHOU S H, et al. Simple Contrastive Graph Clustering. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(10): 13789-13800. [72] WU X X, ZHOU M N, ZHANG W Z.CGCFS: A Comparative Graph Clustering Method Based on Feature and Structural Informa-tion. Expert Systems with Applications, 2025, 274. DOI: 10.1016/j.eswa.2024.126346. [73] YANG X H, LIU Y, ZHOU S H, et al. Cluster-Guided Contrastive Graph Clustering Network. Proceedings of the AAAI Confe-rence on Artificial Intelligence, 2023, 37(9): 10834-10842. [74] PAN E L, KANG Z.Multi-view Contrastive Graph Clustering // Proc of the 35th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2021: 2148-2159. [75] WANG X, LIU N, HAN H, et al. Self-Supervised Heterogeneous Graph Neural Network with Co-contrastive Learning // Proc of the 27th ACM SIGKDD International Conference on Knowledge Disco-very & Data Mining. New York, USA: ACM, 2021: 1726-1736. [76] XIA J, WU L R, WANG G, et al. ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning. Proceedings of the Machine Learning Research, 2022, 162: 24332-24346. [77] LIU Y, YANG X H, ZHOU S H, et al. Hard Sample Aware Network for Contrastive Deep Graph Clustering. Proceedings of the AAAI Conference on Artificial Intelligence, 2023, 37(7): 8914-8922. [78] ZHAO H, YANG X, DENG C.Parameter-Agnostic Deep Graph Clustering. ACM Transactions on Knowledge Discovery from Data, 2024, 18(3). DOI: 10.1145/3633783. [79] PAN S R, HU R Q, LONG G D, et al. Adversarially Regularized Graph Autoencoder for Graph Embedding // Proc of the 27th International Joint Conferences on Artificial Intelligence. San Francisco, USA: IJCAI, 2018: 2609-2615. [80] GAO H C, PEI J, HUANG H.ProGAN: Network Embedding via Proximity Generative Adversarial Network // Proc of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York, USA: ACM, 2019: 1308-1316. [81] JIA Y T, ZHANG Q Q, ZHANG W N, et al. CommunityGAN: Community Detection with Generative Adversarial Nets // Proc of the World Wide Web Conference. New York, USA: ACM, 2019: 784-794. [82] WANG J, CAO J N, LI W, et al. CANE: Community-Aware Network Embedding via Adversarial Training. Knowledge and Information Systems, 2021, 63: 411-438. [83] WANG T, YANG G Y, HE Q J, et al. NCAGC: A Neighborhood Contrast Framework for Attributed Graph Clustering[C/OL].[2024-11-12]. https://arxiv.org/pdf/2206.07897. [84] CUI C H, REN Y Z, PU J Y, et al. Deep Multi-view Subspace Clustering with Anchor Graph // Proc of the 32nd International Joint Conference on Artificial Intelligence. San Francisco, USA: IJCAI, 2023: 3577-3585 [85] LIU Y, TU W X, ZHOU S H, et al. Deep Graph Clustering via Dual Correlation Reduction. Proceedings of the AAAI Conference on Artificial Intelligence, 2022, 36(7): 7603-7611. [86] LIN J Q, CHEN M S, ZHU X R, et al. Dual Information Enhanced Multiview Attributed Graph Clustering. IEEE Transactions on Neural Networks and Learning Systems, 2024. DOI: 10.1109/TNNLS.2024.3401449. [87] LI S J, WANG C J, XU K L, et al. Mutual-view Contrastive Gene-rative Framework for Attribute-Missing Graph Clustering // Proc of the IEEE International Conference on Acoustics, Speech and Signal Processing. Washington, USA: IEEE, 2025. DOI: 10.1109/ICASSP49660.2025.10889050. [88] XU H L, XIA W, GAO Q X, et al. Graph Embedding Clustering: Graph Attention Auto-Encoder with Cluster-Specificity Distribution. Neural Networks, 2021, 142: 221-230. [89] DO T H, NGUYEN D M, DELIGIANNIS N.Graph Auto-Encoder for Graph Signal Denoising // Proc of the IEEE International Conference on Acoustics, Speech and Signal Processing. Washington, USA: IEEE, 2020: 3322-3326. [90] WANG C, PAN S R, LONG G D, et al. MGAE: Marginalized Graph Autoencoder for Graph Clustering // Proc of the ACM Conference on Information and Knowledge Management. New York, USA: ACM, 2017: 889-898. [91] CHIU B, SAHU S K, THOMAS D, et al. Autoencoding Keyword Correlation Graph for Document Clustering // Proc of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, USA: ACL, 2020: 3974-3981. [92] QIN S, JIANG T, WU S, et al. Graph Convolution-Based Deep Clustering for Speech Separation. IEEE Access, 2020, 8: 82571-82580. [93] ZHANG Y W, WANG Z, SHANG J B.ClusterLLM: Large Language Models as a Guide for Text Clustering // Proc of the Confe-rence on Empirical Methods in Natural Language Processing. Stroudsburg, USA: ACL, 2023: 13903-13920. [94] WANG Z D, ZHENG L, LI Y L, et al. Linkage Based Face Clustering via Graph Convolution Network // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2019: 1117-1125. [95] ALWASSEL H, MAHAJAN D, KORBAR B, et al. Self-Supervised Learning by Cross-Modal Audio-Video Clustering // Proc of the 34th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2020: 9758-9770. [96] CAVALLARI S, ZHENG V W, CAI H Y, et al. Learning Community Embedding with Community Detection and Node Embedding on Graphs // Proc of the ACM Conference on Information and Know-ledge Management. New York, USA: ACM, 2017: 377-386. [97] ROZEMBERCZKI B, DAVIES R, SARKAR R, et al. GEMSEC: Graph Embedding with Self Clustering // Proc of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Washington, USA: IEEE, 2019: 65-72. [98] ROY A, SHU J, LI J, et al. GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction // Proc of the 17th ACM International Conference on Web Search and Data Mining. New York, USA: ACM, 2024: 576-585. [99] EKLE O A, EBERLE W.Anomaly Detection in Dynamic Graphs: A Comprehensive Survey. ACM Transactions on Knowledge Disco-very from Data, 2024, 18(8). DOI: 10.1145/366990. [100] HE J W, XU Q Q, JIANG Y B Y, et al. ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 2024, 38(8): 8481-8489. [101] GAO C, ZHENG Y, LI N, et al. A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions. ACM Transactions on Recommender Systems, 2023, 1(1). DOI: 10.1145/356802. [102] CHEN Y J, LIU Z W, LI J, et al. Intent Contrastive Learning for Sequential Recommendation // Proc of the ACM Web Confe-rence. New York, USA: ACM, 2022: 2172-2182. [103] GRUNIG G, DURMUS N, ZHANG Y, et al. Molecular Clus-tering Analysis of Blood Biomarkers in World Trade Center Exposed Community Members with Persistent Lower Respiratory Symptoms. International Journal of Environmental Research and Public Health, 2022, 19(13). DOI: 10.3390/ijerph19138102. [104] XUE H S, MALLAWAARACHCHI V, ZHANG Y J, et al. RepBin: Constraint-Based Graph Representation Learning for Meta-genomic Binning. Proceedings of the AAAI Conference on Artificial Intelligence, 2022, 36(4): 4637-4645. [105] YU Z H, LU Y F, WANG Y H, et al. ZINB-Based Graph Embedding Autoencoder for Single-Cell RNA-Seq Interpretations. Proceedings of the AAAI Conference on Artificial Intelligence, 2022, 36(4): 4671-4679.