A survey of graph continual learning: a unified taxonomy and research landscape

Abstract

Real-world networks, such as social, citation, and Internet of Things (IoT) graphs, evolve continuously, with both their topologies and node attributes changing over time. Conventional graph representation learning typically assumes a static structure and requires full retraining when new data arrive, which often results in catastrophic forgetting (CF) across tasks. Graph continual learning (GCL) aims to incrementally integrate new information from temporal graphs while retaining previously acquired knowledge, thereby enabling long-term and adaptive graph intelligence. Despite significant progress, existing studies on GCL remain fragmented and lack a unified conceptual framework. The growing role of external and pretrained knowledge has introduced a new dimension to graph continual learning, posing new challenges to methods that rely solely on traditional techniques. In this survey, we propose a fourfold taxonomy of GCL methods, encompassing replay-based, regularization-based, architecture-based, and knowledge-based approaches. This taxonomy extends core principles of continual learning and emphasizes the transformative impact of external knowledge on graph learning paradigms. Furthermore, we consolidate the GCL research landscape by summarizing representative applications, task formulations, evaluation protocols, and benchmark datasets, providing a foundation for reproducibility and fair comparison. Finally, we discuss emerging directions in cross-modal graph continual learning, the integration of pretrained knowledge into GCL frameworks, and the evolution of evaluation protocols for real-world scenes.

Graphical abstract

Keywords

graph continual learning / graph learning / continual learning / graph lifelong learning / catastrophic forgetting / pretrained knowledge

References

[1] Xia F, Sun K, Yu S, Aziz A, Wan L, Pan S, Liu H . Graph learning: a survey. IEEE Transactions on Artificial Intelligence, 2021, 2( 2): 109–127

[2] Zhang Z, Cui P, Zhu W . Deep learning on graphs: a survey. IEEE Transactions on Knowledge and Data Engineering, 2022, 34( 1): 249–270

[3] Xue G, Zhong M, Li J, Chen J, Zhai C, Kong R . Dynamic network embedding survey. Neurocomputing, 2022, 472: 212–223

[4] Majeed A, Rauf I . Graph theory: a comprehensive survey about graph theory applications in computer science and social networks. Inventions, 2020, 5( 1): 10

[5] Nettleton D F . Data mining of social networks represented as graphs. Computer Science Review, 2013, 7: 1–34

[6] An Y, Janssen J, Milios E E . Characterizing and mining the citation graph of the computer science literature. Knowledge and Information Systems, 2004, 6( 6): 664–678

[7] Buneman P, Dosso D, Lissandrini M, Silvello G . Data citation and the citation graph. Quantitative Science Studies, 2021, 2( 4): 1399–1422

[8] Rose K, Eldridge S, Chapin L . The internet of things: an overview: understanding the issues and challenges of a more connected world. The Internet Society (ISOC), 2015, 80( 15): 1–53

[9] Long M, Wang J, Ding G, Sun J, Yu P S. Transfer feature learning with joint distribution adaptation. In: Proceedings of the IEEE International Conference on Computer Vision. 2013, 2200–2207

[10] Zhang S, Tong H, Xu J, Maciejewski R . Graph convolutional networks: a comprehensive review. Computational Social Networks, 2019, 6( 1): 11

[11] Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations. 2017

[12] Choudhury S, Holder L, Chin G, Agarwal K, Feo J. A selectivity based approach to continuous pattern detection in streaming graphs. In: Proceedings of the 18th International Conference on Extending Database Technology. 2015, 157–168

[13] Gan X, Tan W, Jia M, Liu J, Zhang Y. XSP: fast SSSP based on communication-computation collaboration. In: Proceedings of the 18th IFIP WG 10.3 International Conference on Network and Parallel Computing. 2021, 53–63

[14] McCloskey M, Cohen N J . Catastrophic interference in connectionist networks: the sequential learning problem. Psychology of Learning and Motivation, 1989, 24: 109–165

[15] Benavides-Prado D, Riddle P. A theory for knowledge transfer in continual learning. In: Proceedings of the 1st Conference on Lifelong Learning Agents. 2022, 647–660

[16] Parisi G I, Kemker R, Part J L, Kanan C, Wermter S . Continual lifelong learning with neural networks: a review. Neural Networks, 2019, 113: 54–71

[17] De Lange M, Aljundi R, Masana M, Parisot S, Jia X, Leonardis A, Slabaugh G, Tuytelaars T . A continual learning survey: defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44( 7): 3366–3385

[18] Hadsell R, Rao D, Rusu A A, Pascanu R . Embracing change: continual learning in deep neural networks. Trends in Cognitive Sciences, 2020, 24( 12): 1028–1040

[19] Ven De Ven G M, Tolias A S. Three scenarios for continual learning. 2019, arXiv preprint arXiv: 1904.07734

[20] Freeman W J . Chaos in the brain: possible roles in biological intelligence. International Journal of Intelligent Systems, 1995, 10( 1): 71–88

[21] Gan X, He M, Deng Q . Massively parallelizing DNA sequencing based on BFS. Basic & Clinical Pharmacology & Toxicology, 2021, 128: 86–86

[22] Ven De Ven G M, Tuytelaars T, Tolias A S . Three types of incremental learning. Nature Machine Intelligence, 2022, 4( 12): 1185–1197

[23] Oren G, Wolf L. In defense of the learning without forgetting for task incremental learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021, 2209–2218

[24] Belouadah E, Popescu A, Kanellos I . A comprehensive study of class incremental learning algorithms for visual tasks. Neural Networks, 2021, 135: 38–54

[25] Masana M, Liu X, Twardowski B, Menta M, Bagdanov A D, Van De Weijer J . Class-incremental learning: survey and performance evaluation on image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45( 5): 5513–5533

[26] Zhou D W, Wang Q W, Qi Z H, Ye H J, Zhan D C, Liu Z . Class-incremental learning: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46( 12): 9851–9873

[27] Mirza M J, Masana M, Possegger H, Bischof H. An efficient domain-incremental learning approach to drive in all weather conditions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022, 3000–3010

[28] Gan X, Wu G, Zeng R, Si J, Liu J, Dong D, Gong C, Liu C, Li T. FT-topo: architecture-driven folded-triangle partitioning for communication-efficient graph processing. In: Proceedings of the 37th International Conference on Supercomputing. 2023, 240–250

[29] Lee B, Plaisant C, Parr C S, Fekete J D, Henry N. Task taxonomy for graph visualization. In: Proceedings of 2006 AVI Workshop on BEyond Time and Errors: Novel Evaluation Methods for Information Visualization. 2006, 1–5

[30] Saket B, Simonetto P, Kobourov S, Börner K . Node, node-link, and node-link-group diagrams: an evaluation. IEEE Transactions on Visualization and Computer Graphics, 2014, 20( 12): 2231–2240

[31] Min C, Bu Y, Wu D, Ding Y, Zhang Y . Identifying citation patterns of scientific breakthroughs: a perspective of dynamic citation process. Information Processing & Management, 2021, 58( 1): 102428

[32] Eom Y H, Fortunato S . Characterizing and modeling citation dynamics. PLoS One, 2011, 6( 9): e24926

[33] Rajgopal S, Venkatachalam M, Kotha S . The value relevance of network advantages: The case of E–commerce firms. Journal of Accounting Research, 2003, 41( 1): 135–162

[34] Linda S L . Social commerce–E-commerce in social media context. International Scholarly and Scientific Research & Innovation, 2010, 4( 12): 2213–2218

[35] Yang Z, Zhang G, Wu J, Yang J, Sheng Q Z, Xue S, Zhou C, Aggarwal C, Peng H, Hu W B, Hancock E, Liò P . State of the art and potentialities of graph-level learning. ACM Computing Surveys, 2024, 57( 2): 28

[36] Gan X, Guo P, Wu G, Li T. GreenBFS: space-efficient BFS engine for power-aware graph processing. In: Proceedings of 2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). 2022, 489–496

[37] Drews J . Drug discovery: a historical perspective. Science, 2000, 287( 5460): 1960–1964

[38] Hughes J P, Rees S, Kalindjian S B, Philpott K L . Principles of early drug discovery. British Journal of Pharmacology, 2011, 162( 6): 1239–1249

[39] Sliwoski G, Kothiwale S, Meiler J, Lowe E W Jr . Computational methods in drug discovery. Pharmacological Reviews, 2014, 66( 1): 334–395

[40] Johnson R, Li M M, Noori A, Queen O, Zitnik M . Graph artificial intelligence in medicine. Annual Review of Biomedical Data Science, 2024, 7( 1): 345–368

[41] Bhatti U A, Tang H, Wu G, Marjan S, Hussain A . Deep learning with graph convolutional networks: an overview and latest applications in computational intelligence. International Journal of Intelligent Systems, 2023, 2023( 1): 8342104

[42] Yuan Q, Guan S U, Ni P, Luo T, Man K L, Wong P, Chang V. Continual graph learning: a survey. 2023, arXiv preprint arXiv: 2301.12230

[43] Febrinanto F G, Xia F, Moore K, Thapa C, Aggarwal C . Graph lifelong learning: a survey. IEEE Computational Intelligence Magazine, 2023, 18( 1): 32–51

[44] Zhang X, Song D, Tao D. Continual learning on graphs: challenges, solutions, and opportunities. 2024, arXiv preprint arXiv: 2402.11565

[45] Tian Z, Zhang D, Dai H N. Continual learning on graphs: a survey. 2024, arXiv preprint arXiv: 2402.06330

[46] Li Z, Ren K, Jiang X, Li B, Zhang H, Li D. Domain generalization using pretrained models without fine-tuning. 2022, arXiv preprint arXiv: 2203.04600

[47] Gan X . TianheGraph: topology-aware graph processing. ACM Transactions on Architecture and Code Optimization, 2025, 22( 3): 112

[48] Han X, Zhang Z, Ding N, Gu Y, Liu X, Huo Y, Qiu J, Yao Y, Zhang A, Zhang L, Han W, Huang M, Jin Q, Lan Y, Liu Y, Liu Z, Lu Z, Qiu X, Song R, Tang J, Wen J, Yuan J, Zhao W, Zhu J . Pre-trained models: past, present and future. AI Open, 2021, 2: 225–250

[49] Wang H, Li J, Wu H, Hovy E, Sun Y . Pre-trained language models and their applications. Engineering, 2023, 25: 51–65

[50] Zeng Z, Tan H, Zhang H, Li J, Zhang Y, Zhang L. An extensive study on pre-trained models for program understanding and generation. In: Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis. 2022, 39–51

[51] Longpre S, Hou L, Vu T, Webson A, Chung H W, Tay Y, Zhou D, Le Q V, Zoph B, Wei J, Roberts A. The flan collection: designing data and methods for effective instruction tuning. In: Proceedings of the 40th International Conference on Machine Learning. 2023, 941

[52] Peng B, Li C, He P, Galley M, Gao J. Instruction tuning with GPT-4. 2023, arXiv preprint arXiv: 2304.03277

[53] Ge C, Huang R, Xie M, Lai Z, Song S, Li S, Huang G . Domain adaptation via prompt learning. IEEE Transactions on Neural Networks and Learning Systems, 2025, 36( 1): 1160–1170

[54] Zhou L, Ye M, Zhang D, Zhu C, Ji L . Prototype-based multisource domain adaptation. IEEE Transactions on Neural Networks and Learning Systems, 2021, 33( 10): 5308–5320

[55] Hamilton W L. Graph Representation Learning. San Rafael: Morgan & Claypool Publishers, 2020

[56] Chen F, Wang Y C, Wang B, Kuo C C J . Graph representation learning: a survey. APSIPA Transactions on Signal and Information Processing, 2020, 9( 1): e15

[57] Khoshraftar S, An A . A survey on graph representation learning methods. ACM Transactions on Intelligent Systems and Technology, 2024, 15( 1): 19

[58] Gan X, Li T, Wang Y, Zhang Q, Yi Y, Gong C, Liu J, Lu K. TianheEngine: hierarchy-aware adaptive partitioning system for trillion-scale graph processing. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 2025, 1785–1799

[59] Wu Z, Pan S, Chen F, Long G, Zhang C, Yu P S . A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32( 1): 4–24

[60] Scarselli F, Gori M, Tsoi A C, Hagenbuchner M, Monfardini G . The graph neural network model. IEEE Transactions on Neural Networks, 2009, 20( 1): 61–80

[61] Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M . Graph neural networks: a review of methods and applications. AI Open, 2020, 1: 57–81

[62] Kim G, Xiao C, Konishi T, Ke Z, Liu B . A theoretical study on solving continual learning. Advances in Neural Information Processing Systems, 2022, 35: 5065–5079

[63] Wickramasinghe B, Saha G, Roy K . Continual learning: A review of techniques, challenges, and future directions. IEEE Transactions on Artificial Intelligence, 2023, 5( 6): 2526–2546

[64] Grossberg S. Studies of Mind and Brain: Neural Principles of Learning, Perception, Development, Cognition, and Motor Control. Dordrecht: Springer, 2012

[65] Mermillod M, Bugaiska A, Bonin P . The stability-plasticity dilemma: investigating the continuum from catastrophic forgetting to age-limited learning effects. Frontiers in Psychology, 2013, 4: 504

[66] Song L, Sun X, Gan X, Pan Y, Han X, Ma J, Liu J, Shang X. Metapath and hypergraph structure-based multi-channel graph contrastive learning for student performance prediction. In: Proceedings of the 34th International Joint Conference on Artificial Intelligence. 2025, 689

[67] Weiss K, Khoshgoftaar T M, Wang D . A survey of transfer learning. Journal of Big Data, 2016, 3( 1): 9

[68] Torrey L, Shavlik J. Transfer learning. In: Olivas E S, Guerrero J D M, Sober M M, Benedito J R M, Lopez A J, eds. Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods and Techniques. Hershey: IGI Global Scientific Publishing, 2010, 242–264

[69] Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, Xiong H, He Q . A comprehensive survey on transfer learning. Proceedings of the IEEE, 2021, 109( 1): 43–76

[70] Zhang Y, Yang Q . An overview of multi-task learning. National Science Review, 2018, 5( 1): 30–43

[71] Zhang Y, Yang Q . A survey on multi-task learning. IEEE Transactions on Knowledge and Data Engineering, 2022, 34( 12): 5586–5609

[72] Caruana R . Multitask learning. Machine Learning, 1997, 28( 1): 41–75

[73] Gan X, Wang Z . How to transfer strided data into accelerator. AATCC Journal of Research, 2021, 8( 1_suppl): 202–209

[74] Carliner S. An Overview of Online Learning. 2nd ed. Amherst: The Human Resource Development Press, 2004, 216

[75] Hoi S C H, Sahoo D, Lu J, Zhao P . Online learning: a comprehensive survey. Neurocomputing, 2021, 459: 249–289

[76] Shen Z, Liu J, He Y, Zhang X, Xu R, Yu H, Cui P. Towards out-of-distribution generalization: a survey. 2021, arXiv preprint arXiv: 2108.13624

[77] Hendrycks D, Basart S, Mu N, Kadavath S, Wang F, Dorundo E, Desai R, Zhu T, Parajuli S, Guo M, Song D, Steinhardt J, Gilmer J. The many faces of robustness: a critical analysis of out-of-distribution generalization. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021, 8320–8329

[78] Li H, Wang X, Zhang Z, Zhu W . Out-of-distribution generalization on graphs: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025, 47( 11): 10490–10512

[79] Achiam J, Adler S, Agarwal S, Ahmad L, Akkaya I, , et al. GPT-4 technical report. 2023, arXiv preprint arXiv: 2303.08774

[80] Anil R, Borgeaud S, Alayrac J B, Yu J, Soricut R, , et al. Gemini: a family of highly capable multimodal models. 2023, arXiv preprint arXiv: 2312.11805

[81] Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V. RoBERta: a robustly optimized BERT pretraining approach. 2019, arXiv preprint arXiv: 1907.11692

[82] Shi H, Xu Z, Wang H, Qin W, Wang W, Wang Y, Wang Z, Ebrahimi S, Wang H . Continual learning of large language models: a comprehensive survey. ACM Computing Surveys, 2026, 58( 5): 120

[83] Song L, Li H, Chen S, Gan X, Shi B, Ma J, Pan Y, Wang X, Shang X. Multi-scale temporal neural network for stock trend prediction enhanced by temporal hyepredge learning. In: Proceedings of the 34th International Joint Conference on Artificial Intelligence. 2025, 3272–3280

[84] Liu H, Di S, Li H, Jian X, Wang Y, Chen L. A selective learning method for temporal graph continual learning. 2025, arXiv preprint arXiv:2503.01580

[85] Daruna A, Gupta M, Sridharan M, Chernova S . Continual learning of knowledge graph embeddings. IEEE Robotics and Automation Letters, 2021, 6( 2): 1128–1135

[86] Choi S, Kim W, Kim S, In Y, Kim S, Park C. DSLR: diversity enhancement and structure learning for rehearsal-based graph continual learning. In: Proceedings of the ACM Web Conference 2024. 2024, 733–744

[87] Guo J, Ni Z, Zhu Y, Tang S . E-CGL: an efficient continual graph learner. Frontiers of Information Technology & Electronic Engineering, 2025, 26( 8): 1441–1453

[88] Zhang D, Downer J, Chen C, Wang R . Efficient and robust continual graph learning for graph classification in biology. IEEE Transactions on Signal and Information Processing over Networks, 2025, 11: 952–964

[89] Xie Z, Luo Q, Huang Y, Yin Y, Zhang J, Pang J . Experience replay with cohesive-subgraph awareness for continual graph learning in IoT. Digital Communications and Networks, 2026, 12( 3): 417–427

[90] Vignesh Malarkkan A, Wang D, Bai H, Fu Y. Incremental causal graph learning for online cyberattack detection in cyber-physical infrastructures. 2025, arXiv preprint arXiv: 2507.14387

[91] Perini M, Ramponi G, Carbone P, Kalavri V. Learning on streaming graphs with experience replay. In: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing. 2022, 470–478

[92] Zhou F, Cao C. Overcoming catastrophic forgetting in graph neural networks with experience replay. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence. 2021, 4714–4722

[93] Li M, Yang X, Chen Y, Zhou S, Gu Y, Hu Q . ReFNet: rehearsal-based graph lifelong learning with multi-resolution framelet graph neural networks. Information Sciences, 2025, 700: 121856

[94] Zhang X, Song D, Tao D . Ricci curvature-based graph sparsification for continual graph representation learning. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35( 12): 17398–17410

[95] Zhang X, Song D, Tao D. Sparsified subgraph memory for continual graph representation learning. In: Proceedings of 2022 IEEE International Conference on Data Mining (ICDM). 2022, 1335–1340

[96] Wang J, Song G, Wu Y, Wang L. Streaming graph neural networks via continual learning. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020, 1515–1524

[97] Ahrabian K, Xu Y, Zhang Y, Wu J, Wang Y, Coates M. Structure aware experience replay for incremental learning in graph-based recommender systems. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021, 2832–2836

[98] Zhang X, Song D, Chen Y, Tao D. Topology-aware embedding memory for continual learning on expanding networks. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2024, 4326–4337

[99] Su J, Zou D, Wu C. On the limitation and experience replay for GNNs in continual learning. In: Proceedings of the 3rd Conference on Lifelong Learning Agents. 2025, 342–366

[100] Chen X, Wang J, Xie K. TrafficStream: a streaming traffic flow forecasting framework based on graph neural networks and continual learning. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence. 2021, 3620–3626

[101] Hoang T D, Tung D V, Nguyen D H, Nguyen B S, Nguyen H H, Le H. Universal graph continual learning. 2023, arXiv preprint arXiv: 2308.13982

[102] Ran B, Gan X. GAP: adaptive DFS branch-level parallel subgraph matching. In: Proceedings of the 21st International Conference on Advanced Intelligent Computing Technology and Applications. 2025, 351–362

[103] Han X, Feng Z, Ning Y. A topology-aware graph coarsening framework for continual graph learning. In: Proceedings of the 38th International Conference on Neural Information Processing Systems. 2024, 4212

[104] Liu Y, Qiu R, Huang Z. CaT: balanced continual graph learning with graph condensation. In: Proceedings of 2023 IEEE International Conference on Data Mining (ICDM). 2023, 1157–1162

[105] Niu C, Pang G, Chen L. Graph continual learning with debiased lossless memory replay. In: Proceedings of ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain - Including the 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024). 2024, 1808–1815

[106] Feng J, Yang X, Zheng D, Han W, Fang B, Liao Q . Overcoming catastrophic forgetting in federated continual graph learning for resource-limited mobile devices. IEEE Transactions on Mobile Computing, 2025, 24( 10): 11151–11163

[107] Liu Y, Qiu R, Tang Y, Yin H, Huang Z . PUMA: efficient continual graph learning for node classification with graph condensation. IEEE Transactions on Knowledge and Data Engineering, 2025, 37( 1): 449–461

[108] Wang J, Zhu W, Song G, Wang L. Streaming graph neural networks with generative replay. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022, 1878–1888

[109] Qiao Z, Xiao J, Sun Q, Xiao M, Luo X, Xiong H. Towards continuous reuse of graph models via holistic memory diversification. 2024, arXiv preprint arXiv: 2406.07413

[110] Chen J, Ma Z, Fu S, Feng M, Wirjanto T S, Ou W. Towards effective open-set graph class-incremental learning. In: Proceedings of the 33rd ACM International Conference on Multimedia. 2025, 11956–11965

[111] Tong H, Faloutsos C, Pan J Y . Random walk with restart: fast solutions and applications. Knowledge and Information Systems, 2008, 14( 3): 327–346

[112] Gan X, Li T, Gong C, Li D, Dong D, Liu J, Lu K . GraphCSR: a degree-equalized CSR format for large-scale graph processing. Proceedings of the VLDB Endowment, 2025, 18( 11): 4255–4268

[113] Li D, Zhang A, Gao J, Qi B. An efficient memory module for graph few-shot class-incremental learning. In: Proceedings of the 38th International Conference on Neural Information Processing Systems. 2024, 4133

[114] Ding S, Feng F, He X, Liao Y, Shi J, Zhang Y . Causal incremental graph convolution for recommender system retraining. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35( 4): 4718–4728

[115] Kou X, Lin Y, Liu S, Li P, Zhou J, Zhang Y. Disentangle-based continual graph representation learning. In: Proceedings of 2020 Conference on Empirical Methods in Natural Language Processing. 2020, 2961–2972

[116] Zhu Y, Hu M, Wu D. Federated continual graph learning. In: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2. 2025, 4203–4213

[117] Shahane A H, Kumar S. GOTHAM: graph class incremental learning framework under weak supervision. 2025, arXiv preprint arXiv: 2504.04954

[118] Tan Z, Ding K, Guo R, Liu H. Graph few-shot class-incremental learning. In: Proceedings of the 15th ACM International Conference on Web Search and Data Mining. 2022, 987–996

[119] Zhang X, Song D, Tao D . Hierarchical prototype networks for continual graph representation learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45( 4): 4622–4636

[120] Song L, Li J, Guan S, Kong Y. Instance-prototype affinity learning for non-exemplar continual graph learning. 2025, arXiv preprint arXiv: 2505.10040

[121] Francies M L, Khalil A T, Amer H M, Ata M M . A framework for continual learning in real-time traffic forecasting utilizing spatial–temporal graph convolutional recurrent networks. Complex & Intelligent Systems, 2025, 11( 10): 420

[122] Jha K, Xun G, Zhang A . Continual representation learning for evolving biomedical bipartite networks. Bioinformatics, 2021, 37( 15): 2190–2197

[123] Song H J, Park S B . Enriching translation-based knowledge graph embeddings through continual learning. IEEE Access, 2018, 6: 60489–60497

[124] Li L, Jin Z, He Y, Jin D, Zhang Y, Duan H, Zhang X, Tao Z, Tash N. Learning to evolve: Bayesian-guided continual knowledge graph embedding. 2025, arXiv preprint arXiv: 2508.02426

[125] Liu H, Yang Y, Wang X. Overcoming catastrophic forgetting in graph neural networks. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence. 2021, 8653–8661

[126] Su J, Zou D, Zhang Z, Wu C. Towards robust graph incremental learning on evolving graphs. In: Proceedings of the International Conference on Machine Learning. 2023, 32728–32748

[127] Yin J, Sun K, Wu H. Unbiased online curvature approximation for regularized graph continual learning. 2025, arXiv preprint arXiv: 2509.12727

[128] Gan X, Li T, Gong C, Liu J, Lu K. YH-Light: yielding hierarchy-aware partitioner for large-scale graph processing. In: Proceedings of the 39th ACM International Conference on Supercomputing. 2025, 1106–1116

[129] Aich A. Elastic weight consolidation (EWC): nuts and bolts. 2021, arXiv preprint arXiv: 2105.04093

[130] Kim S, Yun S, Kang J. DyGRAIN: an incremental learning framework for dynamic graphs. In: Proceedings of the 31st International Joint Conference on Artificial Intelligence. 2022, 3157–3163

[131] Dong S, Hong X, Tao X, Chang X, Wei X, Gong Y. Few-shot class-incremental learning via relation knowledge distillation. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence. 2021, 1255–1263

[132] Lu B, Gan X, Yang L, Zhang W, Fu L, Wang X. Geometer: graph few-shot class-incremental learning via prototype representation. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022, 1152–1161

[133] Wang Y, Zhang Y, Coates M. Graph structure aware contrastive knowledge distillation for incremental learning in recommender systems. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021, 3518–3522

[134] Tang W, Qing L, Wang P, Li L, Peng Y . Graph-based interactive knowledge distillation for social relation continual learning. Neurocomputing, 2025, 634: 129860

[135] Xu Y, Zhang Y, Guo W, Guo H, Tang R, Coates M. GraphSAIL: graph structure aware incremental learning for recommender systems. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020, 2861–2868

[136] Cui Y, Wang Y, Sun Z, Liu W, Jiang Y, Han K, Hu W. Lifelong embedding learning and transfer for growing knowledge graphs. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence. 2023, 4217–4224

[137] Hoffmann M, Galke L, Scherp A. Open-world lifelong graph learning. In: Proceedings of 2023 International Joint Conference on Neural Networks (IJCNN). 2023, 1–9

[138] Sun L, Ye J, Peng H, Wang F, Yu P S. Self-supervised continual graph learning in adaptive riemannian spaces. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence. 2023, 4633–4642

[139] Liu Y, Hong X, Tao X, Dong S, Shi J, Gong Y. Structural knowledge organization and transfer for class-incremental learning. In: Proceedings of the 3rd ACM International Conference on Multimedia in Asia. 2021, 18

[140] Liu J, Ke W, Wang P, Shang Z, Gao J, Li G, Ji K, Liu Y. Towards continual knowledge graph embedding via incremental distillation. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence. 2024, 8759–8768

[141] Sun G, Zhang X, Ni J, Song D. HERO: heterogeneous continual graph learning via meta-knowledge distillation. 2025, arXiv preprint arXiv: 2505.17458

[142] Gan X, Li T, Wu L, Zhang Q, Song L, Yang B, Liu J, Lu K. GraphCom: communication hierarchy-aware graph engine for distributed model training. In: Proceedings of the ACM on Web Conference 2025. 2025, 786–795

[143] Zhang P, Yan Y, Li C, Wang S, Xie X, Song G, Kim S. Continual learning on dynamic graphs via parameter isolation. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2023, 601–611

[144] Kong L, Vasiloudis T, Yun S, Xie H, Song X. Dynamic mixture-of-experts for incremental graph learning. 2025, arXiv preprint arXiv: 2508.09974

[145] Xia J, Li D, Gu H, Lu T, Zhang P, Gu N. Incremental graph convolutional network for collaborative filtering. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021, 2170–2179

[146] Gao Q, Kausar S, Zhang H . Incremental-learning-based graph neural networks on edge-forwarding devices for network intrusion detection. Alexandria Engineering Journal, 2025, 126: 81–89

[147] Wei H, Hu G, Bai W, Xia S, Pan Z . Lifelong representation learning in dynamic attributed networks. Neurocomputing, 2019, 358: 1–9

[148] You H, Liu B. MCIGLE: multimodal exemplar-free class-incremental graph learning. In: Proceedings of the 18th International Conference on Knowledge Science, Engineering and Management. 2025, 209–217

[149] Cai J, Wang X, Guan C, Tang Y, Xu J, Zhong B, Zhu W. Multimodal continual graph learning with neural architecture search. In: Proceedings of the ACM Web Conference 2022. 2022, 1292–1300

[150] Wang Q, Zhou T, Yuan Y, Mao R. Prompt-driven continual graph learning. 2025, arXiv preprint arXiv: 2502.06327

[151] Rakaraddi A, Lam S K, Pratama M, De Carvalho M. Reinforced continual learning for graphs. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2022, 1666–1674

[152] Niu C, Pang G, Chen L, Liu B. Replay-and-forget-free graph class-incremental learning: a task profiling and prompting approach. In: Proceedings of the 38th International Conference on Neural Information Processing System. 2024, 2793

[153] Liu J, Zhang X. Task-aware adaptive modulation: a replay-free and resource-efficient approach for continual graph learning. 2025, arXiv preprint arXiv: 2509.00735

[154] Gan X, Li T, Zhang Q, Wu G, Yang B, Gong C, Liu J, Lu K. G_RAPHCSR: a space and time-efficient sparse matrix representation for web-scale graph processing. In: Proceedings of the ACM on Web Conference 2025. 2025, 763–771

[155] Zhou M, Lu J . Continuous graph learning-based self-adaptation for multi-stream concept drift. IEEE Transactions on Cybernetics, 2025, 55( 8): 3760–3773

[156] Luo Y, Huang Z, Zhang Z, Wang Z, Baktashmotlagh M, Yang Y. Learning from the past: continual meta-learning with Bayesian graph neural networks. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence. 2020, 5021–5028

[157] Wang C, Qiu Y, Gao D, Scherer S. Lifelong graph learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022, 13709–13718

[158] Galke L, Franke B, Zielke T, Scherp A. Lifelong learning of graph neural networks for open-world node classification. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN). 2021, 1–8

[159] Gan X . GraphService: topology-aware constructor for large-scale graph applications. ACM Transactions on Architecture and Code Optimization, 2025, 22( 1): 2

[160] Di Ciccio C, Marrella A, Russo A . Knowledge-intensive processes: characteristics, requirements and analysis of contemporary approaches. Journal on Data Semantics, 2015, 4( 1): 29–57

[161] Alvesson M. Knowledge Work and Knowledge-Intensive Firms. Oxford: Oxford University Press, 2004

[162] Gou J, Yu B, Maybank S J, Tao D . Knowledge distillation: a survey. International Journal of Computer Vision, 2021, 129( 6): 1789–1819

[163] Cheng Z, Li Z, Li Y, Song Y, Zhao K, Cheng D, Li J, Cheng H, Yu J X. Can LLMs alleviate catastrophic forgetting in graph continual learning? A systematic study. 2025, arXiv preprint arXiv: 2505.18697

[164] Ivanovski A, Jovanovik M, Stojanov R, Trajanov D . Knowledge graph based recommender for automatic playlist continuation. Information, 2023, 14( 9): 510

[165] Chaudhry A, Dokania P K, Ajanthan T, Torr P H S. Riemannian walk for incremental learning: Understanding forgetting and intransigence. In: Proceedings of the 15th European Conference on Computer Vision (ECCV). 2018, 556–572

[166] Chaudhry A, Ranzato M A, Rohrbach M, Elhoseiny M. Efficient lifelong learning with A-GEM. 2018, arXiv preprint arXiv: 1812.00420

[167] Gan X, Wu G, Liu C, Si J, Chen X, Yang B, Li T. TianheQueries: ultra-fast and scalable graph queries on Tianhe supercomputer. In: Proceedings of the 24th IEEE Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys). 2022, 1153–1158

[168] Sen P, Namata G, Bilgic M, Getoor L, Gallagher B, Eliassi-Rad T . Collective classification in network data. AI Magazine, 2008, 29( 3): 93–106

[169] Hamilton W L, Ying R, Leskovec J. Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017, 1025–1035

[170] Mikolov T, Sutskever I, Chen K, Corrado G, Dean J. Distributed representations of words and phrases and their compositionality. In: Proceedings of the 27th International Conference on Neural Information Processing Systems. 2013, 3111–3119

[171] Wang K, Shen Z, Huang C, Wu C H, Dong Y, Kanakia A . Microsoft academic graph: when experts are not enough. Quantitative Science Studies, 2020, 1( 1): 396–413

[172] Chiang W L, Liu X Q, Si S, Li Y, Bengio S, Hsieh C J. Cluster-GCN: an efficient algorithm for training deep and large graph convolutional networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019, 257–266

[173] Szklarczyk D, Gable A L, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva N T, Morris J H, Bork P, Jensen L J, Von Mering C . STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research, 2019, 47( D1): D607–D613

[174] Zaki A, Attia M, Hegazy D, Amin S . Comprehensive survey on dynamic graph models. International Journal of Advanced Computer Science and Applications(ijacsa), 2016, 7( 2): 573–582

[175] Gan X, Zhang Y, Wang R, Li T, Xiao T, Zeng R, Liu J, Lu K . TianheGraph: customizing graph search for graph500 on Tianhe supercomputer. IEEE Transactions on Parallel and Distributed Systems, 2022, 33( 4): 941–951

[176] Zhang C, Song D, Huang C, Swami A, Chawla N V. Heterogeneous graph neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019, 793–803

[177] Wang X, Bo D, Shi C, Fan S, Ye Y, Yu P S . A survey on heterogeneous graph embedding: methods, techniques, applications and sources. IEEE Transactions on Big Data, 2023, 9( 2): 415–436

[178] Kulinski S, Inouye D I. Towards explaining distribution shifts. In: Proceedings of the International Conference on Machine Learning. 2023, 17931–17952

[179] Gan X, Wu G, Qiu S, Xiong F, Si J, Fang J, Dong D, Gong C, Li T, Wang Z. GraphCube: interconnection hierarchy-aware graph processing. In: Proceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming. 2024, 160–174

[180] Ko H, Lee S, Park Y, Choi A . A survey of recommendation systems: recommendation models, techniques, and application fields. Electronics, 2022, 11( 1): 141

[181] Isinkaye F O, Folajimi Y O, Ojokoh B A . Recommendation systems: principles, methods and evaluation. Egyptian Informatics Journal, 2015, 16( 3): 261–273

[182] Resnick P, Varian H R . Recommender systems. Communications of the ACM, 1997, 40( 3): 56–58

[183] Gan X, Li T, Zhang Q, Yang B, Chen X, Liu J. SuperCSR: a space-time-efficient CSR representation for large-scale graph applications on supercomputers. In: Proceedings of the 53rd International Conference on Parallel Processing. 2024, 158–167

[184] Kumar P, Thakur R S . Recommendation system techniques and related issues: a survey. International Journal of Information Technology, 2018, 10( 4): 495–501

[185] Gong X, Feng Q, Zhang Y, Qin J, Ding W, Li B, Jiang P, Gai K. Real-time short video recommendation on mobile devices. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2022, 3103–3112

[186] Schein A I, Popescul A, Ungar L H, Pennock D M. Methods and metrics for cold-start recommendations. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2002, 253–260

[187] Lu J, Liu A, Dong F, Gu F, Gama J, Zhang G . Learning under concept drift: a review. IEEE Transactions on Knowledge and Data Engineering, 2019, 31( 12): 2346–2363

[188] Gama J, Žliobaitė I, Bifet A, Pechenizkiy M, Bouchachia A . A survey on concept drift adaptation. ACM Computing Surveys (CSUR), 2014, 46( 4): 44

[189] Kossinets G, Watts D J . Empirical analysis of an evolving social network. Science, 2006, 311( 5757): 88–90

[190] Harary F, Gupta G . Dynamic graph models. Mathematical and Computer Modelling, 1997, 25( 7): 79–87

[191] Yap M J, Pexman P M, Wellsby M, Hargreaves I S, Huff M J . An abundance of riches: cross-task comparisons of semantic richness effects in visual word recognition. Frontiers in Human Neuroscience, 2012, 6: 72

[192] Frank M R, Wang D, Cebrian M, Rahwan I . The evolution of citation graphs in artificial intelligence research. Nature Machine Intelligence, 2019, 1( 2): 79–85

[193] Ugander J, Karrer B, Backstrom L, Marlow C. The anatomy of the Facebook social graph. 2011, arXiv preprint arXiv: 1111.4503

[194] Gan X, Zhang Q, Li T, Gong C, Lu K. GraphWorld: ultra-fast graph engine for world-wide web searching. In: Proceedings of the 33rd ACM International Conference on Multimedia. 2025, 11918–11927

[195] Thakor A V . The design of financial systems: an overview. Journal of Banking & Finance, 1996, 20( 5): 917–948

[196] Hasan T . Graph neural network models for detecting fraudulent insurance claims in healthcare systems. American Journal of Advanced Technology and Engineering Solutions, 2022, 2( 1): 88–109

[197] Srivastava A, Bhattacharya P, Singh A, Mathur A, Prakash O, Pradhan R. A distributed credit transfer educational framework based on blockchain. In: Proceedings of 2018 Second International Conference on Advances in Computing, Control and Communication Technology (IAC3T). 2018, 54–59

[198] Wu G, Gan X. MatchBG: a boundary subgraph-based maximal matching algorithm for bipartite graphs. In: Proceedings of the 29th International Conference on Database Systems for Advanced Applications. 2024, 463–473

[199] Chaudhary K, Yadav J, Mallick B . A review of fraud detection techniques: Credit card. International Journal of Computer Applications, 2012, 45( 1): 39–44

[200] Wang H, Wang Y, Cao Z, Li Z, Xiong G. An overview of blockchain security analysis. In: Proceedings of the 15th International Annual Conference on Cyber Security. 2018, 55–72

[201] Kiffer L, Rajaraman R, Shelat A. A better method to analyze blockchain consistency. In: Proceedings of 2018 ACM SIGSAC Conference on Computer and Communications Security. 2018, 729–744

[202] Camero A, Alba E . Smart city and information technology: a review. Cities, 2019, 93: 84–94

[203] Su K, Li J, Fu H. Smart city and the applications. In: Proceedings of 2011 International Conference on Electronics, Communications and Control (ICECC). 2011, 1028–1031

[204] Barros J, Araujo M, Rossetti R J. Short-term real-time traffic prediction methods: a survey. In: Proceedings of 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MTITS). 2015, 132–139

[205] Yuan H, Li G . A survey of traffic prediction: from spatio-temporal data to intelligent transportation. Data Science and Engineering, 2021, 6( 1): 63–85

[206] Nguyen D C, Ding M, Pathirana P N, Seneviratne A, Li J, Poor H V . Federated learning for internet of things: a comprehensive survey. IEEE Communications Surveys & Tutorials, 2021, 23( 3): 1622–1658

[207] Imteaj A, Thakker U, Wang S, Li J, Amini M H . A survey on federated learning for resource-constrained IoT devices. IEEE Internet of Things Journal, 2022, 9( 1): 1–24

[208] Gan X, Tang Q, Xiong F, Li S, Yang B, Li T. TianheStar: orchestrating SSSP applications on Tianhe supercomputer. In: Proceedings of the 24th IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid). 2024, 534–542

[209] Rao V S, Srinivas K, Sujini G, Kumar G N S . Protein-protein interaction detection: methods and analysis. International Journal of Proteomics, 2014, 2014: 147648

[210] Van Gunsteren W F, Bakowies D, Baron R, Chandrasekhar I, Christen M, Daura X, Gee P, Geerke D P, Glättli A, Hünenberger P H, Kastenholz M A, Oostenbrink C, Schenk M, Trzesniak D, Van Der vegt N F A, Yu H B . Biomolecular modeling: goals, problems, perspectives. Angewandte Chemie International Edition, 2006, 45( 25): 4064–4092

[211] Yao Z J, Dong J, Che Y J, Zhu M F, Wen M, Wang N N, Wang S, Lu A P, Cao D S . TargetNet: a web service for predicting potential drug–target interaction profiling via multi-target SAR models. Journal of Computer-Aided Molecular Design, 2016, 30( 5): 413–424

[212] Bendale A, Boult T. Towards open world recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015, 1893–1902

[213] Jafarzadeh M, Dhamija A R, Cruz S, Li C, Ahmad T, Boult T E. A review of open-world learning and steps toward open-world learning without labels. 2020, arXiv preprint arXiv: 2011.12906

[214] Gan X, Li B, Guo M, Su H . Customizing FFT with FMA for China accelerator. AATCC Journal of Research, 2021, 8( 1_suppl): 210–218

[215] Wang Y, Feng X, Zhou Y, Gan X . A multi-GPU finite volume solver for magnetohydrodynamics-based solar wind simulations. Computer Physics Communications, 2019, 238: 181–193

[216] Kasneci E, Sessler K, Küchemann S, Bannert M, Dementieva D, Fischer F, Gasser U, Groh G, Günnemann S, Hüllermeier E, Krusche S, Kutyniok G, Michaeli T, Nerdel C, Pfeffer J, Poquet O, Sailer M, Schmidt A, Seidel T, Stadler M, Weller J, Kuhn J, Kasneci G . ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 2023, 103: 102274

[217] Naveed H, Khan A U, Qiu S, Saqib M, Anwar S, Usman M, Akhtar N, Barnes N, Mian A . A comprehensive overview of large language models. ACM Transactions on Intelligent Systems and Technology, 2025, 16( 5): 106

[218] Hu W, Liu B, Gomes J, Zitnik M, Liang P, Pande V S, Leskovec J. Strategies for pre-training graph neural networks. 2019, arXiv preprint arXiv:1905.12265

[219] Lu Y, Jiang X, Fang Y, Shi C. Learning to pre-train graph neural networks. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence. 2021, 4276–4284

[220] Gan X, Tan W, Liu J . Bidirectional-bitmap based CSR for reducing large-scale graph space. Journal of Computer Research and Development, 2021, 58( 3): 458–466

[221] Hu E J, Shen Y, Wallis P, Zhu Z A, Li Y, Wang S, Wang L, Chen W . LoRA: low-rank adaptation of large language models. In: Proceedings of the 10th International Conference on Learning Representations, 2022, 1( 2): 3

[222] Pfeiffer J, Ruder S, Vulić I, Ponti E M. Modular deep learning. 2023, arXiv preprint arXiv: 2302.11529

[223] Liu M, Liang K, Zhao Y, Tu W, Zhou S, Gan X, Liu X, He K . Self-supervised temporal graph learning with temporal and structural intensity alignment. IEEE Transactions on Neural Networks and Learning Systems, 2025, 36( 4): 6355–6367

[224] Sarter N B . Multimodal information presentation: design guidance and research challenges. International Journal of Industrial Ergonomics, 2006, 36( 5): 439–445

[225] Rahman W, Hasan K, Lee S, Zadeh A B, Mao C, Morency L P, Hoque E. Integrating multimodal information in large pretrained transformers. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020, 2359–2369

[226] Gao P, Jiang Z, You H, Lu P, Hoi S C H, Wang X, Li H. Dynamic fusion with intra- and inter-modality attention flow for visual question answering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019, 6632–6641

[227] Kim J, Ma M, Pham T, Kim K, Yoo C D. Modality shifting attention network for multi-modal video question answering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020, 10103–10112

[228] Li J, Zhou P, Xiong C, Hoi S C H. Prototypical contrastive learning of unsupervised representations. 2020, arXiv preprint arXiv: 2005.04966

[229] Saunshi N, Plevrakis O, Arora S, Khodak M, Khandeparkar H. A theoretical analysis of contrastive unsupervised representation learning. In: Proceedings of the 36th International Conference on Machine Learning. 2019, 5628–5637

[230] Gong C, Liu J, Bao W, Pan D, Gan X, Li S, Chen X, Xiao T, Yang B, Wang R . Review on ecological construction of domestic high-performance parallel application software in Post Moore Era. Journal of System Simulation, 2022, 34( 10): 2107–2118

[231] Zhou X, Fu C, Huang H, He R . Dynamic graph memory bank for video inpainting. IEEE Transactions on Circuits and Systems for Video Technology, 2024, 34( 11): 10831–10844

[232] Prashanth B S, Kumar M V M, Puneetha B H, Al Muraqab N A, Hoque A, Moonesar I A, Rao A . Adaptive buffering strategies for incremental learning under concept drift in lifestyle disease modeling. IEEE Access, 2025, 13: 174001–174033

[233] Seenivasan L, Islam M, Xu M, Lim C M, Ren H . Task-aware asynchronous multi-task model with class incremental contrastive learning for surgical scene understanding. International Journal of Computer Assisted Radiology and Surgery, 2023, 18( 5): 921–928

[234] Zhang J, Yu J, Tao D . Local deep-feature alignment for unsupervised dimension reduction. IEEE Transactions on Image Processing, 2018, 27( 5): 2420–2432

[235] Grant A M . The significance of task significance: job performance effects, relational mechanisms, and boundary conditions. Journal of Applied Psychology, 2008, 93( 1): 108–124

[236] Miller E J, Rice A K. Systems of Organization: the Control of Task and Sentient Boundaries. London: Routledge, 2013

[237] Gan X, Zhang Y, Zeng R, Liu J, Wang R, Li T, Chen L, Lu K. XTree: traversal-based partitioning for extreme-scale graph processing on supercomputers. In: Proceedings of the 38th IEEE International Conference on Data Engineering (ICDE). 2022, 2046–2059

[238] Kumar J S, Archana B, Muralidharan K, Kumar V S . Graph theory: modelling and analyzing complex system. Metallurgical and Materials Engineering, 2025, 31( 3): 70–77

[239] Diestel R. Graph Theory. Berlin: Springer, 2025

[240] Giri P, Paul S, Debnath B K . A fuzzy graph theory and matrix approach (fuzzy GTMA) to select the best renewable energy alternative in India. Applied Energy, 2024, 358: 122582

[241] Friesl M. Knowledge acquisition strategies and company performance in young high technology companies. British Journal of Management, 2012, 23(3): 325−343

[242] Gagne R M . The acquisition of knowledge. Psychological Review, 1962, 69( 4): 355–365

[243] Liu N, Feng Q, Hu X. Interpretability in graph neural networks. In: Wu L, Cui P, Pei J, Zhao L, eds. Graph Neural Networks: Foundations, Frontiers, and Applications. Singapore: Springer, 2022, 121–147

[244] Dai E, Wang S. Towards self-explainable graph neural network. Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021: 302-311

[245] Helmert M. A planning heuristic based on causal graph analysis. In: Proceedings of the 14th International Conference on International Conference on Automated Planning and Scheduling. 2004, 161–170

[246] Gan X, Guo J, Guo P, Wu G, Si J, Mei S, Liu C, Li T. GraphMedia: communication-balanced graph searching for billion-scale social media access. In: Proceedings of the 31st ACM International Conference on Multimedia. 2023, 8984–8993

[247] Liu F, Panagiotakos D . Real-world data: a brief review of the methods, applications, challenges and opportunities. BMC Medical Research Methodology, 2022, 22( 1): 287

[248] Monti S, Grosso V, Todoerti M, Caporali R . Randomized controlled trials and real-world data: differences and similarities to untangle literature data. Rheumatology, 2018, 57( S7): vii54–vii58

[249] Makady A, De Boer A, Hillege H, Klungel O, Goettsch W . What is real-world data? A review of definitions based on literature and stakeholder interviews. Value in Health, 2017, 20( 7): 858–865

[250] Gan X, Li T, Xiong F, Yang B, Chen X, Gong C, Li S, Lu K, Li Q, Zhang Y . MST: topology-aware message aggregation for exascale graph processing of traversal-centric algorithms. ACM Transactions on Architecture and Code Optimization, 2024, 21( 4): 67

[251] Yannakakis M . Edge-deletion problems. SIAM Journal on Computing, 1981, 10( 2): 297–309

[252] Giavitto J L, Spicher A . Topological rewriting and the geometrization of programming. Physica D: Nonlinear Phenomena, 2008, 237( 9): 1302–1314

[253] Sun Y, Tang K, Minku L L, Wang S, Yao X . Online ensemble learning of data streams with gradually evolved classes. IEEE Transactions on Knowledge and Data Engineering, 2016, 28( 6): 1532–1545

[254] Wang S, Schlobach S, Klein M. What is concept drift and how to measure it? International Conference on Knowledge Engineering and Knowledge Management. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010: 241-256

[255] Zeng Z, Cui L, Qian M, Zhang Z, Wei K . A survey on sliding window sketch for network measurement. Computer Networks, 2023, 226: 109696

[256] Zhu X, Li Y . Insights into the structure-sensitive catalytic performance of palladium–copper alloys in electrochemical urea synthesis. ACS Catalysis, 2023, 13( 23): 15322–15330

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