• Wu W, Zhang W, Gong M and Ma X. Noised Multi-Layer Networks Clustering With Graph Denoising and Structure Learning. IEEE Transactions on Knowledge and Data Engineering. 10.1109/TKDE.2023.3335223. 36:10. (5294-5307).

    https://ieeexplore.ieee.org/document/10330014/

  • Li C, Liu R, Fu J, Zhao Z, Duan H and Zeng Q. (2024). DGNN-MN: Dynamic Graph Neural Network via memory regenerate and neighbor propagation. Applied Intelligence. 10.1007/s10489-024-05500-3. 54:19. (9253-9268). Online publication date: 1-Oct-2024.

    https://link.springer.com/10.1007/s10489-024-05500-3

  • Liu D, Pan Z, Hu S and Cai F. (2024). Distance Enhanced Hypergraph Learning for Dynamic Node Classification. Neural Processing Letters. 10.1007/s11063-024-11645-6. 56:5.

    https://link.springer.com/10.1007/s11063-024-11645-6

  • Gravina A and Bacciu D. Deep Learning for Dynamic Graphs: Models and Benchmarks. IEEE Transactions on Neural Networks and Learning Systems. 10.1109/TNNLS.2024.3379735. 35:9. (11788-11801).

    https://ieeexplore.ieee.org/document/10490120/

  • Peng C, Tang T, Yin Q, Bai X, Lim S and Aggarwal C. Physics-Informed Explainable Continual Learning on Graphs. IEEE Transactions on Neural Networks and Learning Systems. 10.1109/TNNLS.2023.3347453. 35:9. (11761-11772).

    https://ieeexplore.ieee.org/document/10387470/

  • Duan P, Zhou C and Liu Y. Dynamic Graph Representation Learning via Coupling-Process Model. IEEE Transactions on Neural Networks and Learning Systems. 10.1109/TNNLS.2023.3257488. 35:9. (12383-12395).

    https://ieeexplore.ieee.org/document/10081061/

  • Ji S, Liu M, Sun L, Liu C and Zhu T. MemMap: An Adaptive and Latent Memory Structure for Dynamic Graph Learning. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. (1257-1268).

    https://doi.org/10.1145/3637528.3672060

  • Zhong Y, Vu H, Yang T and Adhikari B. Efficient and Effective Implicit Dynamic Graph Neural Network. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. (4595-4606).

    https://doi.org/10.1145/3637528.3672026

  • Du H, Shi L, Chen X, Zhao Y, Zhang H, Yang C, Zhuang F and Kou G. Representation Learning of Temporal Graphs with Structural Roles. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. (654-665).

    https://doi.org/10.1145/3637528.3671854

  • Zhang X, Song D, Chen Y and Tao D. Topology-aware Embedding Memory for Continual Learning on Expanding Networks. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. (4326-4337).

    https://doi.org/10.1145/3637528.3671732

  • Shabani N, Wu J, Beheshti A, Sheng Q, Foo J, Haghighi V, Hanif A and Shahabikargar M. A Comprehensive Survey on Graph Summarization With Graph Neural Networks. IEEE Transactions on Artificial Intelligence. 10.1109/TAI.2024.3350545. 5:8. (3780-3800).

    https://ieeexplore.ieee.org/document/10382477/

  • Zhang P, Yan Y, Zhang X, Kang L, Li C, Huang F, Wang S and Kim S. GPT4Rec: Graph Prompt Tuning for Streaming Recommendation. Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. (1774-1784).

    https://doi.org/10.1145/3626772.3657720

  • Loglisci C, Impedovo A, Calders T and Ceci M. (2024). Heuristic approaches for non-exhaustive pattern-based change detection in dynamic networks. Journal of Intelligent Information Systems. 10.1007/s10844-024-00866-9.

    https://link.springer.com/10.1007/s10844-024-00866-9

  • Guliyev R, Haldar A and Ferhatosmanoglu H. (2024). D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks. Proceedings of the VLDB Endowment. 17:11. (2764-2777). Online publication date: 1-Jul-2024.

    https://doi.org/10.14778/3681954.3681961

  • Peng D and Ji L. (2024). SRM-TGA. Knowledge-Based Systems. 294:C. Online publication date: 21-Jun-2024.

    https://doi.org/10.1016/j.knosys.2024.111763

  • Shen X, Yu J, Liang R, Li Q, Liu S, Du S, Sun J and Liu S. Autobalanced Multitask Node Embedding Framework for Intelligent Education. IEEE Transactions on Neural Networks and Learning Systems. 10.1109/TNNLS.2022.3231421. 35:6. (8653-8667).

    https://ieeexplore.ieee.org/document/10003100/

  • Jiao P, Chen H, Tang H, Bao Q, Zhang L, Zhao Z and Wu H. (2024). Contrastive representation learning on dynamic networks. Neural Networks. 10.1016/j.neunet.2024.106240. 174. (106240). Online publication date: 1-Jun-2024.

    https://linkinghub.elsevier.com/retrieve/pii/S0893608024001643

  • Xiao C, Ji W, Zhang Y and Lv S. (2024). PIDKG: Propagating Interaction Influence on the Dynamic Knowledge Graph for Recommendation. ACM Transactions on the Web. 18:2. (1-26). Online publication date: 31-May-2024.

    https://doi.org/10.1145/3593314

  • Liu M, Tu Z, Su T, Wang X, Xu X and Wang Z. (2024). BehaviorNet: A Fine-grained Behavior-aware Network for Dynamic Link Prediction. ACM Transactions on the Web. 18:2. (1-26). Online publication date: 31-May-2024.

    https://doi.org/10.1145/3580514

  • Feng B, Cheng F, Liu Y, Chang X, Wang X and Jin D. (2024). Community Detection on Social Networks With Sentimental Interaction. International Journal on Semantic Web & Information Systems. 20:1. (1-23). Online publication date: 16-May-2024.

    https://doi.org/10.4018/IJSWIS.341232

  • Liu J, Liu J, Zhao K, Tang Y and Chen W. (2024). TP-GNN: Continuous Dynamic Graph Neural Network for Graph Classification 2024 IEEE 40th International Conference on Data Engineering (ICDE). 10.1109/ICDE60146.2024.00215. 979-8-3503-1715-2. (2848-2861).

    https://ieeexplore.ieee.org/document/10598033/

  • Zhang X and Wang B. (2024). Neighborhood Sampling with Incremental Learning for Dynamic Network Embedding 2024 6th International Conference on Communications, Information System and Computer Engineering (CISCE). 10.1109/CISCE62493.2024.10653376. 979-8-3503-5317-4. (1412-1416).

    https://ieeexplore.ieee.org/document/10653376/

  • Qin M and Yeung D. (2023). Temporal Link Prediction: A Unified Framework, Taxonomy, and Review. ACM Computing Surveys. 56:4. (1-40). Online publication date: 30-Apr-2024.

    https://doi.org/10.1145/3625820

  • Xia J, Li D, Gu H, Lu T, Zhang P, Shang L and Gu N. Neural Kalman Filtering for Robust Temporal Recommendation. Proceedings of the 17th ACM International Conference on Web Search and Data Mining. (836-845).

    https://doi.org/10.1145/3616855.3635837

  • Cherif A, Ammar H, Kalkatawi M, Alshehri S and Imine A. (2024). Encoder–decoder graph neural network for credit card fraud detection. Journal of King Saud University - Computer and Information Sciences. 36:3. Online publication date: 1-Mar-2024.

    https://doi.org/10.1016/j.jksuci.2024.102003

  • Jiang Y and Xia H. (2024). Adversarial attacks against dynamic graph neural networks via node injection. High-Confidence Computing. 10.1016/j.hcc.2023.100185. 4:1. (100185). Online publication date: 1-Mar-2024.

    https://linkinghub.elsevier.com/retrieve/pii/S2667295223000831

  • Khoshraftar S and An A. (2023). A Survey on Graph Representation Learning Methods. ACM Transactions on Intelligent Systems and Technology. 15:1. (1-55). Online publication date: 29-Feb-2024.

    https://doi.org/10.1145/3633518

  • Li H, Jiang H, Ye D, Wang Q, Du L, Zeng Y, yuan L, Wang Y and Chen C. (2024). DHGAT: Hyperbolic representation learning on dynamic graphs via attention networks. Neurocomputing. 10.1016/j.neucom.2023.127038. 568. (127038). Online publication date: 1-Feb-2024.

    https://linkinghub.elsevier.com/retrieve/pii/S092523122301161X

  • Vatter J, Mayer R and Jacobsen H. (2023). The Evolution of Distributed Systems for Graph Neural Networks and Their Origin in Graph Processing and Deep Learning: A Survey. ACM Computing Surveys. 56:1. (1-37). Online publication date: 31-Jan-2024.

    https://doi.org/10.1145/3597428

  • Mei P and Zhao Y. (2024). Dynamic network link prediction with node representation learning from graph convolutional networks. Scientific Reports. 10.1038/s41598-023-50977-6. 14:1.

    https://www.nature.com/articles/s41598-023-50977-6

  • Mao Y, Hao Y, Cao X, Fang Y, Lin X, Mao H and Xu Z. Dynamic Graph Embedding via Meta-Learning. IEEE Transactions on Knowledge and Data Engineering. 10.1109/TKDE.2023.3329238. (1-12).

    https://ieeexplore.ieee.org/document/10304626/

  • Duan G, Lv H, Wang H, Feng G and Li X. (2024). Practical Cyber Attack Detection With Continuous Temporal Graph in Dynamic Network System. IEEE Transactions on Information Forensics and Security. 19. (4851-4864). Online publication date: 1-Jan-2024.

    https://doi.org/10.1109/TIFS.2024.3385321

  • Xie L, Tian H and Shen H. (2024). Learning dynamic embeddings for temporal attributed networks. Knowledge-Based Systems. 10.1016/j.knosys.2023.111308. (111308). Online publication date: 1-Jan-2024.

    https://linkinghub.elsevier.com/retrieve/pii/S0950705123010560

  • Li D, Huang T, Hong J, Hong Y, Wang J, Wang Z and Zhang X. (2024). Event Sparse Net: Sparse Dynamic Graph Multi-representation Learning with Temporal Attention for Event-Based Data. Pattern Recognition and Computer Vision. 10.1007/978-981-99-8546-3_17. (208-219).

    https://link.springer.com/10.1007/978-981-99-8546-3_17

  • Liu Z and Wang Y. (2024). DySDGNN: Representation Learning in Dynamic Signed Directed Networks. Database Systems for Advanced Applications. 10.1007/978-981-97-5572-1_19. (301-310).

    https://link.springer.com/10.1007/978-981-97-5572-1_19

  • Hui Y, Chekol M and Wang S. (2024). Leveraging Graph Embedding for Opinion Leader Detection in Dynamic Social Networks. Artificial Intelligence. ECAI 2023 International Workshops. 10.1007/978-3-031-50485-3_1. (5-22).

    https://link.springer.com/10.1007/978-3-031-50485-3_1

  • Ravanmehr R and Mohamadrezaei R. (2024). Hybrid/Advanced Session-Based Recommender Systems. Session-Based Recommender Systems Using Deep Learning. 10.1007/978-3-031-42559-2_5. (171-244).

    https://link.springer.com/10.1007/978-3-031-42559-2_5

  • Zhang L, Guo Q, Qin Y, Wang X, Huang X and Nie W. (2023). Research on Personnel Safety Risk Early Warning Technology Based on Power Infrastructure Samples 2023 IEEE International Conference on Electrical, Automation and Computer Engineering (ICEACE). 10.1109/ICEACE60673.2023.10442715. 979-8-3503-0961-4. (1696-1701).

    https://ieeexplore.ieee.org/document/10442715/

  • Fard S and Ghassemi M. (2023). Temporal Link Prediction Using Graph Embedding Dynamics 2023 IEEE Ninth Multimedia Big Data (BigMM). 10.1109/BigMM59094.2023.00014. 979-8-3503-6000-4. (48-55).

    https://ieeexplore.ieee.org/document/10411773/

  • Yu L, Sun L, Du B and Lv W. Towards better dynamic graph learning. Proceedings of the 37th International Conference on Neural Information Processing Systems. (67686-67700).

    /doi/10.5555/3666122.3669082

  • Chen F, Li P and Wu C. (2023). DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks. Proceedings of the ACM on Management of Data. 1:4. (1-25). Online publication date: 8-Dec-2023.

    https://doi.org/10.1145/3626724

  • Reha J, Lovisotto G, Russo M, Gravina A and Grohnfeldt C. (2023). Continuous-Time Temporal Graph Learning on Provenance Graphs 2023 IEEE International Conference on Data Mining Workshops (ICDMW). 10.1109/ICDMW60847.2023.00148. 979-8-3503-8164-1. (1131-1140).

    https://ieeexplore.ieee.org/document/10411567/

  • Li X, Wang Z, Chen X, Guo B and Yu Z. (2023). A Hybrid Continuous-Time Dynamic Graph Representation Learning Model by Exploring Both Temporal and Repetitive Information. ACM Transactions on Knowledge Discovery from Data. 17:9. (1-22). Online publication date: 30-Nov-2023.

    https://doi.org/10.1145/3596447

  • Shi J, Cui L, Gu B, Lyu B and Gong S. (2023). State Transition Graph-Based Spatial–Temporal Attention Network for Cell-Level Mobile Traffic Prediction. Sensors. 10.3390/s23239308. 23:23. (9308).

    https://www.mdpi.com/1424-8220/23/23/9308

  • Chen C, Gao D, Zhang Y, Wang Q, Fu Z, Zhang X, Zhu J, Gu Y and Yu G. (2024). NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams. Proceedings of the VLDB Endowment. 10.14778/3632093.3632108. 17:3. (455-468). Online publication date: 1-Nov-2023.

    https://dl.acm.org/doi/10.14778/3632093.3632108

  • Duan P, Ren X and Liu Y. (2023). Multi-relational dynamic graph representation learning. Neurocomputing. 10.1016/j.neucom.2023.126688. 558. (126688). Online publication date: 1-Nov-2023.

    https://linkinghub.elsevier.com/retrieve/pii/S0925231223008111

  • Yu J, Zhang X, Wang H, Wang X, Zhang W and Zhang Y. (2023). FPGN: follower prediction framework for infectious disease prevention. World Wide Web. 26:6. (3795-3814). Online publication date: 1-Nov-2023.

    https://doi.org/10.1007/s11280-023-01205-8

  • Li Y, Zhu J, Zhang C, Yang Y, Zhang J, Qiao Y and Wang H. THGNN: An Embedding-based Model for Anomaly Detection in Dynamic Heterogeneous Social Networks. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. (1368-1378).

    https://doi.org/10.1145/3583780.3615079

  • Ren Y, Ke L, Li D, Xue H, Li Z and Zhou S. Incremental Graph Classification by Class Prototype Construction and Augmentation. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. (2136-2145).

    https://doi.org/10.1145/3583780.3614932

  • Liu Z, Hiranandani G, Qian K, Huang E, Xu Y, Zeng B, Subbian K and Wang S. ForeSeer: Product Aspect Forecasting Using Temporal Graph Embedding. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. (1523-1533).

    https://doi.org/10.1145/3583780.3614887

  • Sharma K, Trivedi R, Sridhar R and Kumar S. Temporal Dynamics-Aware Adversarial Attacks on Discrete-Time Dynamic Graph Models. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. (2023-2035).

    https://doi.org/10.1145/3580305.3599517

  • Su J, Zou D, Zhang Z and Wu C. Towards robust graph incremental learning on evolving graphs. Proceedings of the 40th International Conference on Machine Learning. (32728-32748).

    /doi/10.5555/3618408.3619765

  • Zhang P, Yan Y, Li C, Wang S, Xie X, Song G and Kim S. Continual Learning on Dynamic Graphs via Parameter Isolation. Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. (601-611).

    https://doi.org/10.1145/3539618.3591652

  • Zheng X, Wang Z, Chen C, Zhu F and Qian J. (2023). Freshness or Accuracy, Why Not Both? Addressing Delayed Feedback via Dynamic Graph Neural Networks 2023 IEEE International Conference on Web Services (ICWS). 10.1109/ICWS60048.2023.00059. 979-8-3503-0485-5. (405-414).

    https://ieeexplore.ieee.org/document/10248296/

  • Cheng H, Li Q, Cui Z, Liu S and Pan L. (2023). Dynamic Communications Network Linking Prediction by Disseminating Event Embedding 2023 IEEE International Conference on Web Services (ICWS). 10.1109/ICWS60048.2023.00020. 979-8-3503-0485-5. (57-63).

    https://ieeexplore.ieee.org/document/10248250/

  • Liu Z, Che W, Wang S, Xu J and Yin H. (2023). A large-scale data security detection method based on continuous time graph embedding framework. Journal of Cloud Computing. 10.1186/s13677-023-00460-4. 12:1.

    https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-023-00460-4

  • Du X, Zhang X, Wang S and Huang Z. (2023). Efficient Tree-SVD for Subset Node Embedding over Large Dynamic Graphs. Proceedings of the ACM on Management of Data. 10.1145/3588950. 1:1. (1-26). Online publication date: 26-May-2023.

    https://dl.acm.org/doi/10.1145/3588950

  • Xie S, Li Y, Tam D, Liu X, Ying Q, Lau W, Chiu D and Chen S. GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge Aggregation. Advances in Knowledge Discovery and Data Mining. (28-39).

    https://doi.org/10.1007/978-3-031-33377-4_3

  • Layne J, Carpenter J, Serra E and Gullo F. (2023). Temporal SIR-GN: Efficient and Effective Structural Representation Learning for Temporal Graphs. Proceedings of the VLDB Endowment. 16:9. (2075-2089). Online publication date: 1-May-2023.

    https://doi.org/10.14778/3598581.3598583

  • Wu J, He L, Jia T and Tao L. (2023). Temporal link prediction based on node dynamics. Chaos, Solitons & Fractals. 10.1016/j.chaos.2023.113402. 170. (113402). Online publication date: 1-May-2023.

    https://linkinghub.elsevier.com/retrieve/pii/S096007792300303X

  • He B, He X, Zhang Y, Tang R and Ma C. Dynamically Expandable Graph Convolution for Streaming Recommendation. Proceedings of the ACM Web Conference 2023. (1457-1467).

    https://doi.org/10.1145/3543507.3583237

  • Qin X, Sheikh N, Lei C, Reinwald B and Domeniconi G. (2023). SEIGN: A Simple and Efficient Graph Neural Network for Large Dynamic Graphs 2023 IEEE 39th International Conference on Data Engineering (ICDE). 10.1109/ICDE55515.2023.00218. 979-8-3503-2227-9. (2850-2863).

    https://ieeexplore.ieee.org/document/10184567/

  • Wu C, Wang C, Xu J, Fang Z, Gu T, Wang C, Song Y, Zheng K, Wang X and Zhou G. (2023). Instant Representation Learning for Recommendation over Large Dynamic Graphs 2023 IEEE 39th International Conference on Data Engineering (ICDE). 10.1109/ICDE55515.2023.00014. 979-8-3503-2227-9. (82-95).

    https://ieeexplore.ieee.org/document/10184653/

  • Gao C, Zheng Y, Li N, Li Y, Qin Y, Piao J, Quan Y, Chang J, Jin D, He X and Li Y. (2023). A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions. ACM Transactions on Recommender Systems. 1:1. (1-51). Online publication date: 31-Mar-2023.

    https://doi.org/10.1145/3568022

  • Sánchez D, Servadei L, Kiprit G, Wille R and Ecker W. (2023). A Comprehensive Survey on Electronic Design Automation and Graph Neural Networks: Theory and Applications. ACM Transactions on Design Automation of Electronic Systems. 28:2. (1-27). Online publication date: 31-Mar-2023.

    https://doi.org/10.1145/3543853

  • Mi Q, Wang X and Lin Y. (2023). A double attention graph network for link prediction on temporal graph. Applied Soft Computing. 10.1016/j.asoc.2023.110059. 136. (110059). Online publication date: 1-Mar-2023.

    https://linkinghub.elsevier.com/retrieve/pii/S1568494623000777

  • Wang Y, Chen X, Fang J, Meng Z and Liang S. (2023). Enhancing Conversational Recommendation Systems with Representation Fusion. ACM Transactions on the Web. 17:1. (1-34). Online publication date: 28-Feb-2023.

    https://doi.org/10.1145/3577034

  • Sun X, Deng W, Wei X, Fang D, Li B and Chen X. (2023). Akte-Liquid: Acoustic-based Liquid Identification with Smartphones. ACM Transactions on Sensor Networks. 19:1. (1-24). Online publication date: 28-Feb-2023.

    https://doi.org/10.1145/3551640

  • Wu K, Huang Y, Qiu M, Peng Z and Wang L. (2023). Toward Device-free and User-independent Fall Detection Using Floor Vibration. ACM Transactions on Sensor Networks. 19:1. (1-20). Online publication date: 28-Feb-2023.

    https://doi.org/10.1145/3519302

  • Li J, Yu Z, Zhu Z, Chen L, Yu Q, Zheng Z, Tian S, Wu R and Meng C. Scaling up dynamic graph representation learning via spiking neural networks. Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence. (8588-8596).

    https://doi.org/10.1609/aaai.v37i7.26034

  • Jiao L, Chen J, Liu F, Yang S, You C, Liu X, Li L and Hou B. Graph Representation Learning Meets Computer Vision: A Survey. IEEE Transactions on Artificial Intelligence. 10.1109/TAI.2022.3194869. 4:1. (2-22).

    https://ieeexplore.ieee.org/document/9844822/

  • Febrinanto F, Xia F, Moore K, Thapa C and Aggarwal C. Graph Lifelong Learning: A Survey. IEEE Computational Intelligence Magazine. 10.1109/MCI.2022.3222049. 18:1. (32-51).

    https://ieeexplore.ieee.org/document/10026151/

  • Barros C, Mendonça M, Vieira A and Ziviani A. (2021). A Survey on Embedding Dynamic Graphs. ACM Computing Surveys. 55:1. (1-37). Online publication date: 31-Jan-2023.

    https://doi.org/10.1145/3483595

  • Chowdhury S, Sany M, Ahamed M, Das S, Badal F, Das P, Tasneem Z, Hasan M, Islam M, Ali M, Abhi S, Islam M, Sarker S and Gupta B. (2023). A State-of-the-Art Computer Vision Adopting Non-Euclidean Deep-Learning Models. International Journal of Intelligent Systems. 2023. Online publication date: 1-Jan-2023.

    https://doi.org/10.1155/2023/8674641

  • Dai J, Wu L and Guo K. (2023). A Unified Stream and Batch Graph Computing Model for Community Detection. Computer Supported Cooperative Work and Social Computing. 10.1007/978-981-99-2356-4_9. (110-124).

    https://link.springer.com/10.1007/978-981-99-2356-4_9

  • Xiao C, Lv S, Ji W, Zhang Y, Pan H and Wu L. (2023). PIDE: Propagating Influence of Dynamic Evolution on Interaction Networks for Recommendation. Database Systems for Advanced Applications. 10.1007/978-3-031-30672-3_9. (129-146).

    https://link.springer.com/10.1007/978-3-031-30672-3_9

  • Wu J, Jia T, Wang Y and Tao L. (2022). Significant Ties Graph Neural Networks for Continuous-Time Temporal Networks Modeling 2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta). 10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00050. 979-8-3503-4655-8. (177-185).

    https://ieeexplore.ieee.org/document/10189547/

  • Zhang X, Song D and Tao D. CGLB. Proceedings of the 36th International Conference on Neural Information Processing Systems. (13006-13021).

    /doi/10.5555/3600270.3601215

  • Chen L, Wang L, Zeng C, Liu H and Chen J. (2022). DHGEEP: A Dynamic Heterogeneous Graph-Embedding Method for Evolutionary Prediction. Mathematics. 10.3390/math10224193. 10:22. (4193).

    https://www.mdpi.com/2227-7390/10/22/4193

  • Maleki N, Padmanabhan B and Dutta K. (2022). Representing Social Networks as Dynamic Heterogeneous Graphs 2022 IEEE International Conference on Data Mining Workshops (ICDMW). 10.1109/ICDMW58026.2022.00098. 979-8-3503-4609-1. (1-10).

    https://ieeexplore.ieee.org/document/10031246/

  • Zhang X, Song D and Tao D. (2022). Sparsified Subgraph Memory for Continual Graph Representation Learning 2022 IEEE International Conference on Data Mining (ICDM). 10.1109/ICDM54844.2022.00177. 978-1-6654-5099-7. (1335-1340).

    https://ieeexplore.ieee.org/document/10027629/

  • Zhang Q, Li Q, Chen X, Zhang P, Pan S, Fournier-Viger P and Huang J. (2022). A Dynamic Variational Framework for Open-World Node Classification in Structured Sequences 2022 IEEE International Conference on Data Mining (ICDM). 10.1109/ICDM54844.2022.00081. 978-1-6654-5099-7. (703-712).

    https://ieeexplore.ieee.org/document/10027708/

  • Pang Y, Shan A, Wang Z, Wang M, Li J, Zhang J, Huang T and Liu C. (2022). Sparse‐Dyn: Sparse dynamic graph multirepresentation learning via event‐based sparse temporal attention network. International Journal of Intelligent Systems. 10.1002/int.22967. 37:11. (8770-8789). Online publication date: 1-Nov-2022.

    https://onlinelibrary.wiley.com/doi/10.1002/int.22967

  • Huan C, Song S, Liu Y, Zhang H, Liu H, He C, Chen K, Jiang J and Wu Y. T-GCN. Proceedings of the International Conference on Parallel Architectures and Compilation Techniques. (69-82).

    https://doi.org/10.1145/3559009.3569648

  • Dizaji S, Pashazadeh S, Musevi Niya J and Ferraz de Arruda G. (2022). A Comparative Study of Some Point Process Models for Dynamic Networks. Complexity. 10.1155/2022/1616116. 2022. (1-21). Online publication date: 16-Sep-2022.

    https://www.hindawi.com/journals/complexity/2022/1616116/

  • Wang Z, Li Q, Yu D and Han X. Temporal Graph Transformer for Dynamic Network. Artificial Neural Networks and Machine Learning – ICANN 2022. (694-705).

    https://doi.org/10.1007/978-3-031-15931-2_57

  • Sobolevsky S and Belyi A. (2022). Graph neural network inspired algorithm for unsupervised network community detection. Applied Network Science. 10.1007/s41109-022-00500-z. 7:1.

    https://appliednetsci.springeropen.com/articles/10.1007/s41109-022-00500-z

  • Cao W, Zheng C, Yan Z, He Z and Xie W. (2022). Geometric machine learning: research and applications. Multimedia Tools and Applications. 81:21. (30545-30597). Online publication date: 1-Sep-2022.

    https://doi.org/10.1007/s11042-022-12683-9

  • You J, Du T and Leskovec J. ROLAND: Graph Learning Framework for Dynamic Graphs. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. (2358-2366).

    https://doi.org/10.1145/3534678.3539300

  • Sun M, Zhou K, He X, Wang Y and Wang X. GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. (1717-1727).

    https://doi.org/10.1145/3534678.3539249

  • Zhao J, Kong W, Zhou M, Zhou T, Xu Y and Li M. (2022). Prediction of Urban Taxi Travel Demand by Using Hybrid Dynamic Graph Convolutional Network Model. Sensors. 10.3390/s22165982. 22:16. (5982).

    https://www.mdpi.com/1424-8220/22/16/5982

  • Jin J, Zhang M, Pan M and Fang J. (2022). DynGCF: Augmenting Inactive Users and Items in Dynamic Graph-based Collaborative Filtering 2022 International Joint Conference on Neural Networks (IJCNN). 10.1109/IJCNN55064.2022.9892867. 978-1-7281-8671-9. (1-8).

    https://ieeexplore.ieee.org/document/9892867/

  • Liu Y, Li M, Li X, Giunchiglia F, Feng X and Guan R. Few-shot Node Classification on Attributed Networks with Graph Meta-learning. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. (471-481).

    https://doi.org/10.1145/3477495.3531978

  • Zhou H, Wang W, Liu G and Zhou Q. PointGAT: Graph attention networks for 3D object detection. Intelligent and Converged Networks. 10.23919/ICN.2022.0014. 3:2. (204-216).

    https://ieeexplore.ieee.org/document/9878055/

  • Fan W, Jin R, Lu P, Tian C and Xu R. (2022). Towards event prediction in temporal graphs. Proceedings of the VLDB Endowment. 15:9. (1861-1874). Online publication date: 1-May-2022.

    https://doi.org/10.14778/3538598.3538608

  • Chen J, Wang X and Xu X. (2021). GC-LSTM: graph convolution embedded LSTM for dynamic network link prediction. Applied Intelligence. 52:7. (7513-7528). Online publication date: 1-May-2022.

    https://doi.org/10.1007/s10489-021-02518-9

  • Beddar-Wiesing S. Using local activity encoding for dynamic graph pooling in stuctural-dynamic graphs. Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing. (604-609).

    https://doi.org/10.1145/3477314.3506969

  • Zhou Y, Zheng H, Huang X, Hao S, Li D and Zhao J. (2022). Graph Neural Networks: Taxonomy, Advances, and Trends. ACM Transactions on Intelligent Systems and Technology. 13:1. (1-54). Online publication date: 28-Feb-2022.

    https://doi.org/10.1145/3495161

  • Dou Y. Robust Graph Learning for Misbehavior Detection. Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. (1545-1546).

    https://doi.org/10.1145/3488560.3502213

  • Bielak P, Tagowski K, Falkiewicz M, Kajdanowicz T and Chawla N. (2022). FILDNE. Knowledge-Based Systems. 236:C. Online publication date: 25-Jan-2022.

    https://doi.org/10.1016/j.knosys.2021.107453

  • Zhang X, Song D and Tao D. Hierarchical Prototype Networks for Continual Graph Representation Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 10.1109/TPAMI.2022.3186909. (1-15).

    https://ieeexplore.ieee.org/document/9808404/

  • Yang X, Yang Y, Su J, Sun Y, Wang Z, Fan S, Zhang J and Chen J. Who's Next: Rising Star Prediction via Diffusion of User Interest in Social Networks. IEEE Transactions on Knowledge and Data Engineering. 10.1109/TKDE.2022.3151835. (1-1).

    https://ieeexplore.ieee.org/document/9721599/

  • Yang D, Qu B, Yang J, Wang L and Cudre-Mauroux P. Streaming Graph Embeddings via Incremental Neighborhood Sketching. IEEE Transactions on Knowledge and Data Engineering. 10.1109/TKDE.2022.3149999. (1-1).

    https://ieeexplore.ieee.org/document/9709650/

  • Skarding J, Hellmich M, Gabrys B and Musial K. A Robust Comparative Analysis of Graph Neural Networks on Dynamic Link Prediction. IEEE Access. 10.1109/ACCESS.2022.3175981. 10. (64146-64160).

    https://ieeexplore.ieee.org/document/9777697/

  • Antaris S, Rafailidis D and Gidzijauskas S. (2021). A Deep Graph Reinforcement Learning Model for Improving User Experience in Live Video Streaming 2021 IEEE International Conference on Big Data (Big Data). 10.1109/BigData52589.2021.9671949. 978-1-6654-3902-2. (1787-1796).

    https://ieeexplore.ieee.org/document/9671949/

  • Tian S, Xiong T and Shi L. (2021). Streaming Dynamic Graph Neural Networks for Continuous-Time Temporal Graph Modeling 2021 IEEE International Conference on Data Mining (ICDM). 10.1109/ICDM51629.2021.00171. 978-1-6654-2398-4. (1361-1366).

    https://ieeexplore.ieee.org/document/9679075/

  • Chakaravarthy V, Pandian S, Raje S, Sabharwal Y, Suzumura T and Ubaru S. Efficient scaling of dynamic graph neural networks. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. (1-15).

    https://doi.org/10.1145/3458817.3480858

  • Antaris S, Rafailidis D and Girdzijauskas S. Meta-reinforcement learning via buffering graph signatures for live video streaming events. Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. (385-392).

    https://doi.org/10.1145/3487351.3490973

  • Song C, Shu K and Wu B. (2022). Temporally evolving graph neural network for fake news detection. Information Processing and Management: an International Journal. 58:6. Online publication date: 1-Nov-2021.

    https://doi.org/10.1016/j.ipm.2021.102712

  • Tian S, Wu R, Shi L, Zhu L and Xiong T. Self-supervised Representation Learning on Dynamic Graphs. Proceedings of the 30th ACM International Conference on Information & Knowledge Management. (1814-1823).

    https://doi.org/10.1145/3459637.3482389

  • Zhao T, Ni B, Yu W, Guo Z, Shah N and Jiang M. Action Sequence Augmentation for Early Graph-based Anomaly Detection. Proceedings of the 30th ACM International Conference on Information & Knowledge Management. (2668-2678).

    https://doi.org/10.1145/3459637.3482313

  • Giaretta L, Lekssays A, Carminati B, Ferrari E and Girdzijauskas Š. LiMNet: Early-Stage Detection of IoT Botnets with Lightweight Memory Networks. Computer Security – ESORICS 2021. (605-625).

    https://doi.org/10.1007/978-3-030-88418-5_29

  • Cui Y, Zheng K, Cui D, Xie J, Deng L, Huang F and Zhou X. (2021). METRO. Proceedings of the VLDB Endowment. 15:2. (224-236). Online publication date: 1-Oct-2021.

    https://doi.org/10.14778/3489496.3489503

  • Guo J, Mao X, Lin S, Wei W and Huang H. (2021). Deep kernel supervised hashing for node classification in structural networks. Information Sciences. 10.1016/j.ins.2021.03.068. 569. (1-12). Online publication date: 1-Aug-2021.

    https://linkinghub.elsevier.com/retrieve/pii/S0020025521003194

  • Zhao Y, Qi J, Liu Q and Zhang R. WGCN: Graph Convolutional Networks with Weighted Structural Features. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. (624-633).

    https://doi.org/10.1145/3404835.3462834

  • Ha S and Jeong H. (2021). Unraveling hidden interactions in complex systems with deep learning. Scientific Reports. 10.1038/s41598-021-91878-w. 11:1.

    https://www.nature.com/articles/s41598-021-91878-w

  • Tagowski K, Bielak P and Kajdanowicz T. Embedding Alignment Methods in Dynamic Networks. Computational Science – ICCS 2021. (599-613).

    https://doi.org/10.1007/978-3-030-77961-0_48

  • Zhang Y, Wang X, Shi C, Liu N and Song G. Lorentzian Graph Convolutional Networks. Proceedings of the Web Conference 2021. (1249-1261).

    https://doi.org/10.1145/3442381.3449872

  • Yang S, Huang G, Zhou X, Mak V and Yearwood J. (2021). EWNStream+: Effective and Real-time Clustering of Short Text Streams Using Evolutionary Word Relation Network. International Journal of Information Technology & Decision Making. 10.1142/S0219622021500024. 20:01. (341-370). Online publication date: 1-Jan-2021.

    https://www.worldscientific.com/doi/abs/10.1142/S0219622021500024

  • Georgousis S, Kenning M and Xie X. Graph Deep Learning: State of the Art and Challenges. IEEE Access. 10.1109/ACCESS.2021.3055280. 9. (22106-22140).

    https://ieeexplore.ieee.org/document/9339909/

  • Ji Y, Jia T, Fang Y and Shi C. (2021). Dynamic Heterogeneous Graph Embedding via Heterogeneous Hawkes Process. Machine Learning and Knowledge Discovery in Databases. Research Track. 10.1007/978-3-030-86486-6_24. (388-403).

    https://link.springer.com/10.1007/978-3-030-86486-6_24

  • Zheng C, Zong B, Cheng W, Song D, Ni J, Yu W, Chen H and Wang W. (2021). Node Classification in Temporal Graphs Through Stochastic Sparsification and Temporal Structural Convolution. Machine Learning and Knowledge Discovery in Databases. 10.1007/978-3-030-67664-3_20. (330-346).

    http://link.springer.com/10.1007/978-3-030-67664-3_20

  • Liang S, Liu C, Wang Y, Li H and Li X. DeepBurning-GL. Proceedings of the 39th International Conference on Computer-Aided Design. (1-9).

    https://doi.org/10.1145/3400302.3415645

  • Kurshan E, Shen H and Yu H. (2020). Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & Outlook 2020 Second International Conference on Transdisciplinary AI (TransAI). 10.1109/TransAI49837.2020.00029. 978-1-7281-8699-3. (125-130).

    https://ieeexplore.ieee.org/document/9253149/

  • Zhang Z, Cui P and Zhu W. Deep Learning on Graphs: A Survey. IEEE Transactions on Knowledge and Data Engineering. 10.1109/TKDE.2020.2981333. (1-1).

    https://ieeexplore.ieee.org/document/9039675/

  • Schiller B, Jager S, Hamacher K and Strufe T. StreaM - A Stream-Based Algorithm for Counting Motifs in Dynamic Graphs. Proceedings of the Second International Conference on Algorithms for Computational Biology - Volume 9199. (53-67).

    https://doi.org/10.1007/978-3-319-21233-3_5

  • Bergmann G, Dávid I, Hegedüs Á, Horváth Á, Ráth I, Ujhelyi Z and Varró D. Viatra 3. Proceedings of the 8th International Conference on Theory and Practice of Model Transformations - Volume 9152. (101-110).

    https://doi.org/10.1007/978-3-319-21155-8_8

  • Cuadrado J, Guerra E and Lara J. Reusable Model Transformation Components with bentź. Proceedings of the 8th International Conference on Theory and Practice of Model Transformations - Volume 9152. (59-65).

    https://doi.org/10.1007/978-3-319-21155-8_5

  • Getir S, Grunske L, Bernasko C, Käfer V, Sanwald T and Tichy M. CoWolf --- A Generic Framework for Multi-view Co-evolution and Evaluation of Models. Proceedings of the 8th International Conference on Theory and Practice of Model Transformations - Volume 9152. (34-40).

    https://doi.org/10.1007/978-3-319-21155-8_3

  • Hinkel G. Change Propagation in an Internal Model Transformation Language. Proceedings of the 8th International Conference on Theory and Practice of Model Transformations - Volume 9152. (3-17).

    https://doi.org/10.1007/978-3-319-21155-8_1

  • Corallo A, Fortunato L, Matera M, Alessi M, Camillò A, Chetta V, Giangreco E and Storelli D. Sentiment Analysis for Government. Proceedings of the 11th International Conference on Machine Learning and Data Mining in Pattern Recognition - Volume 9166. (98-112).

    https://doi.org/10.1007/978-3-319-21024-7_7

  • Tan C, Guan J and Zhou S. IKLTSA. Proceedings of the 11th International Conference on Machine Learning and Data Mining in Pattern Recognition - Volume 9166. (70-83).

    https://doi.org/10.1007/978-3-319-21024-7_5

  • Govada A, Joshi P, Mittal S and Sahay S. Hybrid Approach for Inductive Semi Supervised Learning Using Label Propagation and Support Vector Machine. Proceedings of the 11th International Conference on Machine Learning and Data Mining in Pattern Recognition - Volume 9166. (199-213).

    https://doi.org/10.1007/978-3-319-21024-7_14