{"id":62334,"url":"https://github.com/SpaceLearner/Awesome-DynamicGraphLearning","name":"Awesome-DynamicGraphLearning","description":"Awesome papers about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs).","projects_count":150,"last_synced_at":"2026-04-25T01:00:22.049Z","repository":{"id":42177199,"uuid":"385274851","full_name":"SpaceLearner/Awesome-DynamicGraphLearning","owner":"SpaceLearner","description":"Awesome papers about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge 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unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"readme":"# Awesome-DynamicGraphLearning [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)\n\nAwesome papers (codes) about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs) and their applications (i.e. Recommender Systems).\n\n## Survey\n\n* Deep learning for dynamic graphs: models and benchmarks (**TNNLS, 2024**) [[paper](https://ieeexplore.ieee.org/document/10490120)][[code](https://github.com/gravins/dynamic_graph_benchmark)]\n* Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities (**ARXIV, 2023**) [[paper](https://arxiv.org/pdf/2302.01018.pdf)]\n* Graph Neural Networks Designed for Different Graph Types: A Survey (**ARXIV, 2022**) [[paper](https://arxiv.org/pdf/2204.03080.pdf)]\n* Representation Learning for Dynamic Graphs: A Survey (**JMLR, 2020**) [[paper](https://arxiv.org/pdf/1905.11485.pdf)]\n* A Survey on Embedding Dynamic Graphs (**ARXIV, 2021**) [[paper](https://arxiv.org/pdf/2101.01229v1.pdf)]\n* Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (**ARXIV, 2019**) [[paper](https://arxiv.org/pdf/1905.11485v1.pdf)]\n* Nonlinearity + Networks: A 2020 Vision (**ARXIV, 2019**) [[paper](https://arxiv.org/pdf/1911.03805v1.pdf)]\n* Temporal networks (**Physics Report, 2012**) [[paper](https://pdf.sciencedirectassets.com/271542/1-s2.0-S0370157312X00309/1-s2.0-S0370157312000841/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEIL%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJHMEUCIQCKOucVujQP07wkAKxMuMLMSsHqFv%2FFb%2BYWtNrMjUENTwIgWQ3afs%2FTBaPhDmEiZPlrVllfwmAmEN9OMs0ReONp0PIq%2BgMIOxAEGgwwNTkwMDM1NDY4NjUiDHz5nNBj0grdhs2zQirXA6ElLZy%2FDlDSDU86oA74varszz0ma8Dbz0g92LEczi64XvbCAHQmPQIiskdJpYzeQQCoHHQwirdve6OhcF6pTJwGbJ2lL84oSrywuWhi6Z0e9kdtLUw2deUEHp2La7FUeebH%2FnHaHV3BpYfl2%2BXA0Y1zI67VWbtXv6MALP6e9THRpRmS6omIAgiB9u6bOm3NDQ4hC7Cp%2F22gUvRvSOm14Y%2F9s2kk7QcqRxTMDTW94Dbtty2O8Pw54CJulxcOo7Nby7%2FXrarewlMgFBxCwhNteoXaVviFrgl91rtQTq5EnU9HEBntgE0r8z%2F0e%2FGh1JuYvd0aK5FzC2ZTGjFHNq7bx%2BdscwV1QiLkiVHsNKc2CURzGvUx0dRFIud8w3PkH7aZVESvKlNvLyKa%2FgL4TU%2B0n5j92ppZHbC3DfB8kwZV1I1QFzB7mFmhdpoAWFlXXY2xxPPkQqsV1%2BsPanWb9JIgkpBnu5ZO2xmVHPRSlvL%2BUTKvD3Jq0LmqEYo1tFy3F4sYEmGV4vw0RKKo7tOYb8SFgdTw26SVhera5aeLIwSFZYAvv0wRb%2BsXgoPJK51YLI1XTXnNep%2FWNLv1Gem993YkpKZdgmoEpPheKv1%2B85mELU1J82NJeExBXTDumIiTBjqlAUQbPzomGso8OdXiqdTW8V8WaRL%2B0ZgHpf3Kzcb3k2W%2FWSKiA91fU6BA%2FTFUUd2iafG1k%2Bgm8Yvli8YBEroGEXmUOH5IdiIyFTIUL7BfcyvedVwWbBCVoGyLxs48G6KWaVwowy2XYP%2BXHfbDghl6NNzdaUwlWc1blXx%2BkkoUq1ZIwsAzhLrwLthvrB%2BLeKT8IdYOWCDhjSdy%2BsHs6t%2F4WEMwQVHScg%3D%3D\u0026X-Amz-Algorithm=AWS4-HMAC-SHA256\u0026X-Amz-Date=20220422T030440Z\u0026X-Amz-SignedHeaders=host\u0026X-Amz-Expires=300\u0026X-Amz-Credential=ASIAQ3PHCVTY3GYD4RGC%2F20220422%2Fus-east-1%2Fs3%2Faws4_request\u0026X-Amz-Signature=e6c67c850d5b9141253837519a558f2d56ee022ea163233ccf17b88f9815df4f\u0026hash=5c12a8966a05652f2c4464fea3e79f3c7f669cf94a84365674e2e0647371a431\u0026host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61\u0026pii=S0370157312000841\u0026tid=spdf-a3f8bf90-014a-4f05-ba9e-eebf11a58a9a\u0026sid=55bff1c97359b9450988c4c294161bf31292gxrqa\u0026type=client\u0026ua=4c00050055575755530304\u0026rr=6ffb22261cbf968e)]\n\n## Papers\n\n### 2025\n\n* Rethinking Time Encoding via Learnable Transformation Functions (**ICML, 2025**) [[paper](https://arxiv.org/pdf/2505.00887)][[code](https://github.com/chenxi1228/LeTE)]\n* Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding  (**WSDM, 2025**) [[paper](https://arxiv.org/abs/2407.16959)][[code](https://github.com/wangz3066/CorDGT)]\n\n### 2024\n\n* Long Range Propagation on Continuous-Time Dynamic Graphs (**ICML, 2024**) [[paper](https://proceedings.mlr.press/v235/gravina24a.html)][[code](https://github.com/gravins/non-dissipative-propagation-CTDGs)]\n*  LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs? (**SIGKDD, 2024**) [[paper](https://arxiv.org/abs/2310.17110)][[code](https://github.com/wondergo2017/LLM4DyG)]\n* Towards Adaptive Neighborhood for Advancing Temporal Interaction Graph Modeling (**SIGKDD, 2024**) [[paper](https://arxiv.org/pdf/2406.11891)]\n* SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning (**SIGKDD, 2024**) [[paper](https://arxiv.org/pdf/2402.11933)][[code](https://arxiv.org/pdf/2402.11933)]\n* Predicting Long-term Dynamics of Complex Networks via Identifying Skeleton in Hyperbolic Space (**SIGKDD, 2024**) [[code](https://github.com/tsinghua-fib-lab/DiskNet)]\n* Latent Conditional Diffusion-based Data Augmentation for Continuous-Time Dynamic Graph Model (**SIGKDD, 2024**) [[paper](https://arxiv.org/pdf/2407.08500)][[code]()]\n* MemMap: An Adaptive and Latent Memory Structure for Dynamic Graph Learning (**SIGKDD, 2024**)\n* TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation Learning (**IPDPS, 2024**) [[paper](https://arxiv.org/abs/2402.05396)][[code](https://github.com/facebookresearch/taser-tgnn)]\n* Mayfly: a Neural Data Structure for Graph Stream Summarization (**ICLR, 2024, Spotlight**) [[paper](https://openreview.net/attachment?id=n7Sr8SW4bn\u0026name=pdf)]\n* Causality-Inspired Spatial-Temporal Explanations for Dynamic Graph Neural Networks (**ICLR, 2024, Poster**) [[paper](https://openreview.net/attachment?id=AJBkfwXh3u\u0026name=pdf)][[code](https://github.com/kesenzhao/DyGNNExplainer)]\n* FreeDyG: Frequency Enhanced Continuous-Time Dynamic Graph Model for Link Prediction (**ICLR, 2024, Poster**) [[paper](https://openreview.net/attachment?id=82Mc5ilInM\u0026name=pdf)][[code](https://github.com/Tianxzzz/FreeDyG)]\n* PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks (**ICLR, 2024, Poster**) [[paper](https://openreview.net/attachment?id=gjXor87Xfy\u0026name=pdf)][[code](https://github.com/jwsu825/MDGNN_BS)]\n* Hypergraph Dynamic System (**ICLR, 2024, Poster**) [[paper](https://openreview.net/attachment?id=NLbRvr840Q\u0026name=pdf)]\n* Deep Temporal Graph Clustering (**ICLR, 2024, Poster**) [[paper](https://openreview.net/attachment?id=ViNe1fjGME\u0026name=pdf)][[code](https://github.com/MGitHubL/Deep-Temporal-Graph-Clustering)]\n* GraphPulse: Topological representations for temporal graph property prediction (**ICLR, 2024, Poster**) [[paper](https://openreview.net/attachment?id=DZqic2sPTY\u0026name=pdf)][[code](https://github.com/kiarashamsi/GraphPulse)]\n* Beyond Spatio-Temporal Representations: Evolving Fourier Transform for Temporal Graphs (**ICLR, 2024, Poster**) [[paper](https://openreview.net/attachment?id=uvFhCUPjtI\u0026name=pdf)][[code](https://github.com/ansonb/EFT)]\n* HOPE: High-order Graph ODE For Modeling Interacting Dynamics (**ICML, 2024, Poster**) [[paper](https://openreview.net/attachment?id=9iChKP4k32\u0026name=pdf)]\n* Temporal Generalization Estimation in Evolving Graphs (**ICLR, 2024, Poster**) [[paper](https://openreview.net/attachment?id=HFtrXBfNru\u0026name=pdf)]\n* Dynamic Graph Information Bottleneck (**WWW, 2024**) [[paper](https://arxiv.org/pdf/2402.06716.pdf)][[code](https://github.com/RingBDStack/DGIB)]\n* On the Feasibility of Simple Transformer for Dynamic Graph Modeling (**WWW, 2024**) [[paper](https://arxiv.org/pdf/2401.14009.pdf)]\n* Temporal Conformity-aware Hawkes Graph Network for Recommendations (**WWW, 2024**) \n* IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion (**WWW, 2024**)\n* TATKC: A Temporal Graph Neural Network for Fast Approximate Temporal Katz Centrality Ranking (**WWW, 2024**)\n* Efficient exact and approximate betweenness centrality computation for temporal graphs (**WWW, 2024**)\n* Temporal Graph ODEs for Irregularly-Sampled Time Series (**IJCAI, 2024**) [[paper](https://www.ijcai.org/proceedings/2024/445)][[code](https://github.com/gravins/TG-ODE)]\n* Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning (**Neurips 2024 Submission**) [[paper](https://arxiv.org/pdf/2405.14170)][[code](https://anonymous.4open.science/r/LLM-DA-1E6D)]\n* Anomaly Detection in Continuous-Time Temporal Provenance Graphs (**Temporal Graph Learning Workshop @ NeurIPS, 2023**) [[paper](https://openreview.net/pdf?id=88tGIxxhsfn)][[code](https://github.com/JakubReha/ProvCTDG)]\n\n### 2023\n\n* Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts (**Neurips, 2023**) [[paper](https://arxiv.org/abs/2403.05026)][[code](https://github.com/wondergo2017/sild)]\n* DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training (**SC, 2023**) [[paper](https://arxiv.org/abs/2307.07649)][[code](https://github.com/amazon-science/disttgl)]\n* Towards Better Dynamic Graph Learning: New Architecture and Unified Library (**ARXIV, 2023**) [[paper](https://arxiv.org/pdf/2303.13047.pdf)][[code](https://github.com/yule-BUAA/DyGLib)]\n* SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning (**ARXIV, 2023**) [[paper](https://arxiv.org/pdf/2303.03379.pdf)][[code](https://github.com/Graph-COM/SUREL_Plus)]\n* Towards Open Temporal Graph Neural Networks (**ICLR, 2023**) [[paper](https://openreview.net/pdf?id=N9Pk5iSCzAn)][[code](https://github.com/tulerfeng/OTGNet)]\n* Do We Really Need Complicated Model Architectures For Temporal Networks? (**ICLR, 2023**) [[paper](https://openreview.net/pdf?id=ayPPc0SyLv1)][[code](https://github.com/CongWeilin/GraphMixer)]\n* Zebra: When Temporal Graph Neural Networks Meet Temporal Personalized PageRank (**VLDB, 2023**) [[paper](https://www.vldb.org/pvldb/vol16/p1332-li.pdf)][[code](https://github.com/LuckyLYM/Zebra)]\n* Temporal SIR-GN: Eficient and Efective Structural Representation Learning for Temporal Graphs (**VLDB, 2023**) [[paper](https://www.vldb.org/pvldb/vol16/p2075-layne.pdf)][[code](https://github.com/janetlayne2/Temporal-SIR-GN)]\n* SEIGN: A Simple and Efficient Graph Neural Network for Large Dynamic Graphs (**ICDE, 2023**) [[paper](https://ieeexplore.ieee.org/abstract/document/10184567)]\n* A Higher-Order Temporal H-Index for Evolving Networks (**KDD, 2023**) [[paper](https://arxiv.org/pdf/2305.16001.pdf)]\n* Using Motif Transitions for Temporal Graph Generation (**KDD, 2023**) [[paper](https://arxiv.org/pdf/2306.11190.pdf)]\n* Temporal Dynamics Aware Adversarial Attacks on Discrete-Time Graph Models (**KDD, 2023**) [[paper](https://openreview.net/pdf?id=yUY15QBERj)][[code](https://github.com/erdemUB/KDD23-MTM)]\n* Fairness-Aware Continuous Predictions of Multiple Analytics Targets in Dynamic Networks (**KDD, 2023**) [[paper](https://arxiv.org/pdf/2209.01678.pdf)]\n* DyTed: Disentangled Representation Learning for Discrete-time Dynamic Graph (**KDD, 2023**) [[paper](https://arxiv.org/pdf/2210.10592.pdf)]\n* WinGNN: Dynamic Graph Neural Networks with Random Gradient Aggregation Window (**KDD, 2023**) \n* Community-based Dynamic Graph Learning for Popularity Prediction (**KDD, 2023**)\n* An Atentional Multi-scale Co-evolving Model for Dynamic Link Prediction (**WWW, 2023**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3543507.3583396)][[code](https://github.com/tsinghua-fib-lab/AMCNet)]\n* TIGER: Temporal Interaction Graph Embedding with Restarts (**WWW, 2023**) [[paper](https://arxiv.org/pdf/2302.06057.pdf)][[code](https://github.com/yzhang1918/www2023tiger)]\n* HGWaveNet: A Hyperbolic Graph Neural Network for Temporal Link Prediction (**WWW, 2023**) [[paper](https://arxiv.org/pdf/2304.07302.pdf)][[code](https://github.com/TaiLvYuanLiang/HGWaveNet)]\n* Expressive and Efficient Representation Learning for Ranking Links in Temporal Graphs (**WWW, 2023**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3543507.3583476)][[code](https://github.com/susheels/tgrank)]\n* Local Edge Dynamics and Opinion Polarization (**WSDM, 2023**) [[paper](https://arxiv.org/pdf/2111.14020.pdf)][[code](https://github.com/adamlechowicz/opinion-polarization/)]\n* Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs (**WSDM, 2023**) [[paper](https://arxiv.org/pdf/2211.08568.pdf)][[code](https://github.com/RManLuo/GSNOP)]\n* Interpretable Research Interest Shift Detection with Temporal Heterogeneous Graphs (**WSDM, 2023**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3539597.3570453)]\n* Dynamic Heterogeneous Graph Attention Neural Architecture Search (**AAAI, 2023**) [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/26338)][[code](https://github.com/wondergo2017/DHGAS)]\n* Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks (**AAAI, 2023**) [[paper](https://arxiv.org/pdf/2208.10364.pdf)][[code](https://github.com/EdisonLeeeee/SpikeNet)]\n* Hidden Markov Models for Temporal Graph Representation Learning (**ESANN, 2023**) [[paper](https://www.esann.org/sites/default/files/proceedings/2023/ES2023-35.pdf)][[code](https://github.com/nec-research/hidden_markov_model_temporal_graphs)]\n\n\n### 2022\n\n* TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs (**VLDB, 2022**) [[paper](https://arxiv.org/pdf/2203.14883.pdf)][[code](https://github.com/amazon-science/tgl)]\n* Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs (**Neurips, 2022**)[[paper](https://proceedings.neurips.cc/paper_files/paper/2022/file/7dadc855cef7494d5d956a8d28add871-Paper-Conference.pdf)][[code](https://github.com/KimMeen/Neural-Temporal-Walks)]\n* Dynamic Graph Neural Networks Under Spatio-Temporal Distribution Shift (**Neurips, 2022**) [[paper](https://proceedings.neurips.cc/paper_files/paper/2022/hash/2857242c9e97de339ce642e75b15ff24-Abstract-Conference.html)][[code](https://github.com/wondergo2017/DIDA)]\n* Adaptive Data Augmentation on Temporal Graphs (**Neurips, 2022**) [[paper](https://proceedings.neurips.cc/paper/2021/file/0b0b0994d12ad343511adfbfc364256e-Paper.pdf)]\n* Parameter-free Dynamic Graph Embedding for Link Prediction (**Neurips, 2022**) [[paper](https://proceedings.neurips.cc/paper_files/paper/2022/file/b14d7175755b180dc2163e15e3110cb6-Paper-Conference.pdf)][[code](https://github.com/FudanCISL/FreeGEM)]\n* Instant Graph Neural Networks for Dynamic Graphs (**KDD, 2022**) [[paper](https://arxiv.org/pdf/2206.01379.pdf)][[code]()]\n* Disentangled Dynamic Heterogeneous Graph Learning for Opioid Overdose Prediction (**KDD, 2022**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3534678.3539279)][[code]()]\n* ROLAND: Graph Learning Framework for Dynamic Graphs (**KDD, 2022**) [[paper](https://arxiv.org/pdf/2208.07239.pdf)][[code](https://github.com/snap-stanford/roland)]\n* Subset Node Anomaly Tracking over Large Dynamic Graphs (**KDD, 2022**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3534678.3539389)][[code](https://github.com/zjlxgxz/DynAnom)]\n* Streaming Graph Neural Networks via Generative Replay (**KDD, 2022**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3534678.3539336)][[code](https://github.com/Junshan-Wang/SGNN-GR)]\n* Neighborhood-aware Scalable Temporal Network Representation Learning (**LoG, 2022**) [[paper](https://openreview.net/pdf?id=EPUtNe7a9ta)][[code](https://github.com/Graph-COM/Neighborhood-Aware-Temporal-Network)]\n* DisenCTR: Dynamic Graph-based Disentangled Representation for Click-Through Rate Prediction (**SIGIR, 2022**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3477495.3531851)][[code](https://github.com/Fang6ang/DisenCTR)]\n* STAM: A Spatiotemporal Aggregation Method for Graph Neural Network-based Recommendation (**WWW, 2022**) [[paper](https://keg.cs.tsinghua.edu.cn/jietang/publications/WWW22-Yang%20et%20al.-STAM-GNN.pdf)][[code](https://github.com/zyang-16/STAM)]\n* Neural Predicting Higher-order Patterns in Temporal Networks (**WWW, 2022**) [[paper](https://arxiv.org/pdf/2106.06039.pdf)][[code](https://github.com/Graph-COM/Neural_Higher-order_Pattern_Prediction)]\n* TREND: TempoRal Event and Node Dynamics for Graph Representation Learning (**WWW, 2022**) [[paper](https://arxiv.org/pdf/2203.14303.pdf)][[code](https://github.com/WenZhihao666/TREND)]\n* A Viral Marketing-Based Model For Opinion Dynamics in Online Social Networks (**WWW, 2022**) [[paper](https://arxiv.org/pdf/2202.03573.pdf)]\n* EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs (**WSDM, 2022**) [[paper](http://keg.cs.tsinghua.edu.cn/yuxiao/papers/WSDM22-park-evokg.pdf)][[code](https://github.com/NamyongPark/EvoKG)]\n* Finding a Concise, Precise, and Exhaustive Set of Near Bi-Cliques in Dynamic Graphs (**WSDM, 2022**) [[paper](https://arxiv.org/pdf/2110.14875.pdf)][[code](https://github.com/hyeonjeong1/cutnpeel)]\n* Few-shot Link Prediction in Dynamic Networks (**WSDM, 2022**) [[paper](http://www.shichuan.org/doc/120.pdf)]\n* On Generalizing Static Node Embedding to Dynamic Settings (**WSDM, 2022**) [[paper](https://gemslab.github.io/papers/dijin-2021-trg.pdf)]\n* Along the Time: Timeline-traced Embedding for Temporal Knowledge Graph Completion (**CIKM, 2022**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3511808.3557233)][[code](https://github.com/zhangfw123/TLT-KGE)]\n* DA-Net: Distributed Attention Network for Temporal Knowledge Graph Reasoning (**CIKM, 2022**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3511808.3557280)]\n* A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning (**CIKM, 2022**) [[paper](https://arxiv.org/pdf/2208.14073.pdf)]\n* Dynamic Hypergraph Learning for Collaborative Filtering (**CIKM, 2022**) [[paper]](https://dl.acm.org/doi/pdf/10.1145/3511808.3557301)\n\n### 2021\n\n* Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks (**ICLR, 2021**) [[paper](https://openreview.net/pdf?id=KYPz4YsCPj)][[code](https://github.com/snap-stanford/CAW)]\n* Coupled Graph ODE for Learning Interacting System Dynamics (**KDD, 2021**) [[paper](http://web.cs.ucla.edu/~yzsun/papers/2021_KDD_CG_ODE.pdf)][[code](https://github.com/ZijieH/CG-ODE)]\n* Subset Node Representation Learning over Large Dynamic Graphs (**KDD, 2021**) [[paper](https://arxiv.org/pdf/2106.01570.pdf)][[code](https://github.com/zjlxgxz/DynamicPPE)]\n* Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space [[paper](https://arxiv.org/pdf/2107.03767.pdf)][[code](https://github.com/marlin-codes/HTGN-KDD21)]\n* Learning to Walk across Time for Temporal Knowledge Graph Completion (**KDD, 2021**) [[paper](https://arxiv.org/pdf/2012.10595v1.pdf)]\n* Forecasting Interaction Order on Temporal Graphs (**KDD, 2021**) \n* Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning (**SIGIR, 2021**) [[paper](https://arxiv.org/pdf/2104.10353.pdf)][[code](https://github.com/Lee-zix/RE-GCN)]\n* Inductive Representation Learning in Temporal Networks via Mining Neighborhood and Community Influences (**SIGIR, 2021**)\n* TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion [[paper](https://arxiv.org/pdf/2104.08419.pdf)]\n* SDG: A Simplified and Dynamic Graph Neural Network (**SIGIR SHORT, 2021**) [[paper](https://github.com/DongqiFu/SDG/blob/main/paper/SDG_A%20Simplified%20and%20Dynamic%20Graph%20Neural%20Network.pdf)][[code](https://github.com/DongqiFu/SDG)]\n* Temporal Augmented Graph Neural Networks for Session-Based Recommendations (**SIGIR SHORT, 2021**) [[paper](https://www4.comp.polyu.edu.hk/~xiaohuang/docs/Huachi_sigir2021.pdf)]\n* HINTS: Citation Time Series Prediction for New Publications via Dynamic Heterogeneous Information Network Embedding (**WWW, 2021**) [[paper](http://web.cs.ucla.edu/~yzsun/papers/2021_WWW_HINTS.pdf)][[code](https://github.com/songjiang0909/HINTS_code)]\n* TEDIC: Neural Modeling of Behavioral Patterns in Dynamic Social Interaction Networks (**WWW, 2021**) [[paper](http://snap.stanford.edu/tedic/files/www21_tedic.pdf)]\n* Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs (**AAAI, 2021**) [[paper](https://arxiv.org/pdf/2104.02228.pdf)]\n* Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks (**AAAI, 2021**) [[paper](https://arxiv.org/pdf/2012.08740.pdf)][[code](https://github.com/InterpretableClustering/InterpretableClustering)]\n* Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay (**AAAI, 2021**) [[paper](https://arxiv.org/pdf/2003.09908.pdf)]\n* Learning and Updating Node Embedding on Dynamic Heterogeneous Information Network (**WSDM, 2021**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3437963.3441745)]\n* F-FADE: Frequency Factorization for Anomaly Detection in Edge Streams (**WSDM, 2021**) [[paper](https://cs.stanford.edu/people/jure/pubs/ffade-wsdm21.pdf)][[code](https://github.com/snap-stanford/F-FADE)]\n* Cache-based GNN System for Dynamic Graphs (**CIKM 2021**) [[paper]]\n* Self-supervised Representation Learning on Dynamic Graphs (**CIKM 2021**)[[paper]]\n* Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer [[paper](https://arxiv.org/pdf/2108.06625.pdf)][[code](https://github.com/DyGRec/TGSRec)]\n* Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs (**CIKM 2021**) [[paper](https://arxiv.org/pdf/2005.07427.pdf)]\n* Neural Higher-order Pattern (Motif) Prediction in Temporal Networks (**ARXIV, 2021**) [[paper](https://arxiv.org/pdf/2106.06039.pdf)]\n\n### 2020\n\n* Inductive Representation Learning on Temporal Graphs (**ICLR, 2020**) [[paper](https://arxiv.org/pdf/2002.07962.pdf)][[code](https://github.com/StatsDLMathsRecomSys/Isnductive-representation-learning-on-temporal-graphs)]\n* Temporal Graph Networks for Deep Learning on Dynamic Graphs (**ICML Workshop, 2020**) [[paper](https://arxiv.org/pdf/2006.10637v1.pdf)][[code](https://github.com/twitter-research/tgn)]\n* A Data-Driven Graph Generative Model for Temporal Interaction Networks (**KDD, 2020**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3394486.3403082)][[code](https://github.com/davidchouzdw/TagGen)]\n* Dynamic Knowledge Graph based Multi-Event Forecasting (**KDD, 2020**) [[paper](https://yue-ning.github.io/docs/KDD20-glean.pdf)][[code](https://github.com/amy-deng/glean)]\n* Laplacian Change Point Detection for Dynamic Graphs (**KDD, 2020**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3394486.3403077)][[code](https://github.com/shenyangHuang/LAD)]\n* Algorithmic Aspects of Temporal Betweenness (**KDD, 2020**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3394486.3403259)][[code](https://fpt.akt.tu-berlin.de/software/temporal_betweenness/)]\n* Heterogeneous Graph Transformer (**WWW, 2020**) [[paper](https://arxiv.org/pdf/2003.01332.pdf)][[code](https://github.com/acbull/pyHGT)]\n* Streaming Graph Neural Network (**SIGIR, 2020**) [[paper](https://arxiv.org/pdf/1810.10627.pdf)][[code](https://github.com/alge24/DyGNN)]\n* Next-item Recommendation with Sequential Hypergraphs (**SIGIR, 2020**) [[paper](http://www.public.asu.edu/~kding9/pdf/SIGIR2020_HyperRec.pdf)][[code](https://github.com/wangjlgz/HyperRec)]\n* Temporal Network Embedding with High-Order Nonlinear Information (**AAAI, 2020**) [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/5993)]\n* Motif-Preserving Temporal Network Embedding (**IJCAI, 2020**) [[paper](https://www.ijcai.org/proceedings/2020/0172.pdf)]\n* Dynamic Graph Collaborative Filtering (**ICDM, 2020**) [[paper](https://arxiv.org/pdf/2101.02844.pdf)][[code](https://github.com/CRIPAC-DIG/DGCF)]\n* DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks (**WSDM, 2020**) [[papr](https://dl.acm.org/doi/pdf/10.1145/3336191.3371845)][[code](https://github.com/aravindsankar28/DySAT)]\n* Learning and Updating Node Embedding on Dynamic Heterogeneous Information Network (**WSDM, 2020**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3437963.3441745)][[code]()]\n* Continuous-Time Dynamic Graph Learning via Neural Interaction Processes (**CIKM, 2020**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3340531.3411946)]\n* tdGraphEmbed: Temporal Dynamic Graph-Level Embedding (**CIKM, 2020**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3340531.3411953)][[code](https://github.com/moranbel/tdGraphEmbed)]\n* Streaming Graph Neural Network via Continue Learning (**CIKM, 2020**) [[paper](https://arxiv.org/pdf/2009.10951.pdf)][[code](https://github.com/Junshan-Wang/ContinualGNN)]\n* Disentangle-based Continual Graph Representation Learning (**EMNLP, 2020**) [[paper](https://arxiv.org/pdf/2010.02565.pdf)][[code](https://github.com/KXY-PUBLIC/DiCGRL)]\n* TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion (**EMNLP, 2020**) [[paper](https://aclanthology.org/2020.emnlp-main.462.pdf)][[code](https://github.com/JiapengWu/TeMP)]\n* Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs (**EMNLP, 2020**) [[paper](https://aclanthology.org/2020.emnlp-main.541.pdf)][[code](https://github.com/INK-USC/RE-Net)]\n* EPNE: Evolutionary Pattern Preserving Network Embedding (**ECAI, 2020**) [[paper](http://ecai2020.eu/papers/528_paper.pdf)]\n* GloDyNE: Global Topology Preserving Dynamic Network Embedding (**TKDE, 2020**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9302718)][[code](https://github.com/houchengbin/GloDyNE)]\n* Dynamic Heterogeneous Information Network Embedding with Meta-path based Proximity (**TKDE, 2020**) [[paper](https://yuanfulu.github.io/publication/TKDE-DyHNE.pdf)][[code](https://github.com/rootlu/DyHNE)]\n* Lifelong Graph Learning (**ARXIV, 2020**) [[paper](https://arxiv.org/pdf/2009.00647.pdf)]\n\n\n\n### 2019\n\n* Variational Graph Recurrent Neural Networks (**NeurIPS, 2019**) [[paper](https://papers.nips.cc/paper/2019/file/a6b8deb7798e7532ade2a8934477d3ce-Paper.pdf)][[code](https://github.com/VGraphRNN/VGRNN)]\n* Recurrent Space-time Graph Neural Networks (**NeurIPS, 2019**) [[paper](http://export.arxiv.org/pdf/1904.05582#:~:text=Our%20recurrent%20neural%20graph%20ef%EF%AC%81ciently%20processes%20information%20in,in%20space-time%20using%20a%20backbone%20deep%20neural%20network.)][[code](https://github.com/IuliaDuta/RSTG)]\n* DyRep: Learning Representations over Dynamic Graphs (**ICLR, 2019**) [[paper](https://openreview.net/pdf?id=HyePrhR5KX)]\n* Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks (**KDD, 2019**) [[paper](https://arxiv.org/pdf/1908.01207.pdf)][[code](https://github.com/srijankr/jodie)]\n* Learning Dynamic Context Graphs for Predicting Social Events (**KDD, 2019**) [[paper](https://yue-ning.github.io/docs/KDD19-dengA.pdf)][[code](https://github.com/amy-deng/DynamicGCN)]\n* EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs (**AAAI, 2019**) [[paper](https://arxiv.org/pdf/1902.10191.pdf)][[code](https://github.com/IBM/EvolveGCN)]\n* Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems (**WWW, 2019**) [[paper](https://arxiv.org/pdf/1904.04381.pdf)]\n* Real-Time Streaming Graph Embedding Through Local Actions (**WWW, 2019**) [[paper](https://nickduffield.net/download/papers/DL4G-SDE-2019.pdf)]\n* Dynamic Hypergraph Neural Networks (**IJCAI, 2019**) [[paper](https://www.ijcai.org/Proceedings/2019/0366.pdf)][[code](https://github.com/iMoonLab/DHGNN#:~:text=%20DHGNN%3A%20Dynamic%20Hypergraph%20Neural%20Networks%20%201,%28Zhilin%20Yang%2C%20William%20W.%20-%20Cohen%2C...%20More%20)]\n* Node Embedding over Temporal Graphs (**IJCAI, 2019**) [[paper](https://www.ijcai.org/proceedings/2019/0640.pdf)][[code](https://github.com/urielsinger/tNodeEmbed#:~:text=Node%20Embedding%20over%20Temporal%20Graphs.%20Uriel%20Singer%2C%20Ido,for%20nodes%20in%20any%20%28un%29directed%2C%20%28un%29weighted%20temporal%20graph.)]\n* Temporal Network Embedding with Micro- and Macro-dynamics (**CIKM, 2019**) [[paper](https://par.nsf.gov/servlets/purl/10148548)][[code](https://github.com/rootlu/MMDNE)]\n\n\n### 2018\n\n* NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks (**KDD, 2018**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3219819.3220024)][[code](https://github.com/kdmsit/NetWalk)]\n* Embedding Temporal Network via Neighborhood Formation (**KDD, 2018**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3219819.3220054)][[code]()]\n* Dynamic Network Embedding by Modeling Triadic Closure Process (**AAAI, 2018**) [[paper](http://yangy.org/works/dynamictriad/dynamic_triad.pdf)][[code](https://github.com/luckiezhou/DynamicTriad)]\n* Continuous-Time Dynamic Network Embeddings (**WWW, 2018**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3184558.3191526)][[code](https://github.com/Shubhranshu-Shekhar/ctdne)]\n* Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embedding (**IJCAI, 2018**) [[paper](https://www.ijcai.org/proceedings/2018/0288.pdf)]\n\n### 2017\n\n* Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs (**ICML, 2017**) [[paper](http://proceedings.mlr.press/v70/trivedi17a/trivedi17a.pdf)][[code](https://github.com/rstriv/Know-Evolve)]\n* The Co-Evolution Model for Social Network Evolving and Opinion Migration (**KDD, 2017**) [[paper](http://web.cs.ucla.edu/~yzsun/papers/2017_kdd_coevolution.pdf)[code]()]\n* Attributed Network Embedding for Learning in a Dynamic Environment (**CIKM, 2017**) [[paper](https://arxiv.org/pdf/1706.01860.pdf)][[code](https://github.com/gaoghc/DANE)]\n\n## Tools\n\n### General Graph Learning\n* [Deep Graph Library](https://www.dgl.ai/)\n* [Pytorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/)\n* [Pytorch Geometric Temporal](https://pytorch-geometric-temporal.readthedocs.io/en/latest/notes/introduction.html)\n* [Stellar Graph](https://stellargraph.readthedocs.io/en/stable/)\n* [GraphVite](https://graphvite.io/)\n\n### Knowledge Graph\n* [DGL-KE](https://dglke.dgl.ai/doc/)\n* [OpenKE](https://github.com/thunlp/OpenKE)\n\n### Recommender System\n* [RecBole](https://www.recbole.io/)\n\n","created_at":"2024-06-28T07:49:03.826Z","updated_at":"2026-04-25T01:00:22.057Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Tools","Papers","Survey"],"sub_categories":["General Graph Learning","2019","2020","Knowledge Graph","2024","2023","2022","2021","2018","2017","Recommender System","2025"],"projects_url":"https://awesome.ecosyste.ms/api/v1/lists/spacelearner%2Fawesome-dynamicgraphlearning/projects"}