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https://github.com/SpaceLearner/Awesome-DynamicGraphLearning

Awesome papers about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs).
https://github.com/SpaceLearner/Awesome-DynamicGraphLearning
List: Awesome-DynamicGraphLearning
deep-learning dynamic-graph-embedding dynamic-network-embedding graph-neural-network graph-neural-networks temporal-graph temporal-network
Last synced: about 1 month ago
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Awesome papers about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs).
Host: GitHub
URL: https://github.com/SpaceLearner/Awesome-DynamicGraphLearning
Owner: SpaceLearner
Created: 2021-07-12T14:25:04.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2024-05-28T08:27:32.000Z (4 months ago)
Last Synced: 2024-08-03T09:01:30.366Z (about 1 month ago)
Topics: deep-learning, dynamic-graph-embedding, dynamic-network-embedding, graph-neural-network, graph-neural-networks, temporal-graph, temporal-network
Language: Shell
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Watchers: 10
Forks: 66
Open Issues: 0
Metadata Files:
- Readme: README.md
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ultimate-awesome - Awesome-DynamicGraphLearning - Awesome papers about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs). (Other Lists / PowerShell Lists)
README

        # Awesome-DynamicGraphLearning [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)

Awesome papers (codes) about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs) and their applications (i.e. Recommender Systems).

## Survey

* Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities (**ARXIV, 2023**) [[paper](https://arxiv.org/pdf/2302.01018.pdf)]

* Graph Neural Networks Designed for Different Graph Types: A Survey (**ARXIV, 2022**) [[paper](https://arxiv.org/pdf/2204.03080.pdf)]

* Representation Learning for Dynamic Graphs: A Survey (**JMLR, 2020**) [[paper](https://arxiv.org/pdf/1905.11485.pdf)]

* A Survey on Embedding Dynamic Graphs (**ARXIV, 2021**) [[paper](https://arxiv.org/pdf/2101.01229v1.pdf)]

* Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (**ARXIV, 2019**) [[paper](https://arxiv.org/pdf/1905.11485v1.pdf)]

* Nonlinearity + Networks: A 2020 Vision (**ARXIV, 2019**) [[paper](https://arxiv.org/pdf/1911.03805v1.pdf)]

* 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&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220422T030440Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTY3GYD4RGC%2F20220422%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=e6c67c850d5b9141253837519a558f2d56ee022ea163233ccf17b88f9815df4f&hash=5c12a8966a05652f2c4464fea3e79f3c7f669cf94a84365674e2e0647371a431&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0370157312000841&tid=spdf-a3f8bf90-014a-4f05-ba9e-eebf11a58a9a&sid=55bff1c97359b9450988c4c294161bf31292gxrqa&type=client&ua=4c00050055575755530304&rr=6ffb22261cbf968e)]

## Papers

### 2024

* 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)]

* Predicting Long-term Dynamics of Complex Networks via Identifying Skeleton in Hyperbolic Space (**SIGKDD, 2024**) [[code](https://github.com/tsinghua-fib-lab/DiskNet)]

* Latent Conditional Diffusion-based Data Augmentation for Continuous-Time Dynamic Graph Model (**SIGKDD, 2024**) [[paper](https://arxiv.org/pdf/2407.08500)][[code]()]

* MemMap: An Adaptive and Latent Memory Structure for Dynamic Graph Learning (**SIGKDD, 2024**)

* 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)]

* Mayfly: a Neural Data Structure for Graph Stream Summarization (**ICLR, 2024, Spotlight**) [[paper](https://openreview.net/attachment?id=n7Sr8SW4bn&name=pdf)]

* Causality-Inspired Spatial-Temporal Explanations for Dynamic Graph Neural Networks (**ICLR, 2024, Poster**) [[paper](https://openreview.net/attachment?id=AJBkfwXh3u&name=pdf)][[code](https://github.com/kesenzhao/DyGNNExplainer)]

* FreeDyG: Frequency Enhanced Continuous-Time Dynamic Graph Model for Link Prediction (**ICLR, 2024, Poster**) [[paper](https://openreview.net/attachment?id=82Mc5ilInM&name=pdf)][[code](https://github.com/Tianxzzz/FreeDyG)]

* PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks (**ICLR, 2024, Poster**) [[paper](https://openreview.net/attachment?id=gjXor87Xfy&name=pdf)][[code](https://github.com/jwsu825/MDGNN_BS)]

* Hypergraph Dynamic System (**ICLR, 2024, Poster**) [[paper](https://openreview.net/attachment?id=NLbRvr840Q&name=pdf)]

* Deep Temporal Graph Clustering (**ICLR, 2024, Poster**) [[paper](https://openreview.net/attachment?id=ViNe1fjGME&name=pdf)][[code](https://github.com/MGitHubL/Deep-Temporal-Graph-Clustering)]

* GraphPulse: Topological representations for temporal graph property prediction (**ICLR, 2024, Poster**) [[paper](https://openreview.net/attachment?id=DZqic2sPTY&name=pdf)][[code](https://github.com/kiarashamsi/GraphPulse)]

* Beyond Spatio-Temporal Representations: Evolving Fourier Transform for Temporal Graphs (**ICLR, 2024, Poster**) [[paper](https://openreview.net/attachment?id=uvFhCUPjtI&name=pdf)][[code](https://github.com/ansonb/EFT)]

* HOPE: High-order Graph ODE For Modeling Interacting Dynamics (**ICML, 2024, Poster**) [[paper](https://openreview.net/attachment?id=9iChKP4k32&name=pdf)]

* Temporal Generalization Estimation in Evolving Graphs (**ICLR, 2024, Poster**) [[paper](https://openreview.net/attachment?id=HFtrXBfNru&name=pdf)]

* Dynamic Graph Information Bottleneck (**WWW, 2024**) [[paper](https://arxiv.org/pdf/2402.06716.pdf)][[code](https://github.com/RingBDStack/DGIB)]

* On the Feasibility of Simple Transformer for Dynamic Graph Modeling (**WWW, 2024**) [[paper](https://arxiv.org/pdf/2401.14009.pdf)]

* Temporal Conformity-aware Hawkes Graph Network for Recommendations (**WWW, 2024**) 

* IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion (**WWW, 2024**)

* TATKC: A Temporal Graph Neural Network for Fast Approximate Temporal Katz Centrality Ranking (**WWW, 2024**)

* Efficient exact and approximate betweenness centrality computation for temporal graphs (**WWW, 2024**)

* 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)]

### 2023

* 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)]

* 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)]

* 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)]

* Towards Open Temporal Graph Neural Networks (**ICLR, 2023**) [[paper](https://openreview.net/pdf?id=N9Pk5iSCzAn)][[code](https://github.com/tulerfeng/OTGNet)]

* 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)]

* 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)]

* 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)]

* SEIGN: A Simple and Efficient Graph Neural Network for Large Dynamic Graphs (**ICDE, 2023**) [[paper](https://ieeexplore.ieee.org/abstract/document/10184567)]

* A Higher-Order Temporal H-Index for Evolving Networks (**KDD, 2023**) [[paper](https://arxiv.org/pdf/2305.16001.pdf)]

* Using Motif Transitions for Temporal Graph Generation (**KDD, 2023**) [[paper](https://arxiv.org/pdf/2306.11190.pdf)]

* 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)]

* Fairness-Aware Continuous Predictions of Multiple Analytics Targets in Dynamic Networks (**KDD, 2023**) [[paper](https://arxiv.org/pdf/2209.01678.pdf)]

* DyTed: Disentangled Representation Learning for Discrete-time Dynamic Graph (**KDD, 2023**) [[paper](https://arxiv.org/pdf/2210.10592.pdf)]

* WinGNN: Dynamic Graph Neural Networks with Random Gradient Aggregation Window (**KDD, 2023**) 

* Community-based Dynamic Graph Learning for Popularity Prediction (**KDD, 2023**)

* 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)]

* TIGER: Temporal Interaction Graph Embedding with Restarts (**WWW, 2023**) [[paper](https://arxiv.org/pdf/2302.06057.pdf)][[code](https://github.com/yzhang1918/www2023tiger)]

* 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)]

* 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)]

* Local Edge Dynamics and Opinion Polarization (**WSDM, 2023**) [[paper](https://arxiv.org/pdf/2111.14020.pdf)][[code](https://github.com/adamlechowicz/opinion-polarization/)]

* 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)]

* Interpretable Research Interest Shift Detection with Temporal Heterogeneous Graphs (**WSDM, 2023**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3539597.3570453)]

* 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)]

### 2022

* 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)]

* 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)]

* Adaptive Data Augmentation on Temporal Graphs (**Neurips, 2022**) [[paper](https://proceedings.neurips.cc/paper/2021/file/0b0b0994d12ad343511adfbfc364256e-Paper.pdf)]

* 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)]

* Instant Graph Neural Networks for Dynamic Graphs (**KDD, 2022**) [[paper](https://arxiv.org/pdf/2206.01379.pdf)][[code]()]

* Disentangled Dynamic Heterogeneous Graph Learning for Opioid Overdose Prediction (**KDD, 2022**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3534678.3539279)][[code]()]

* ROLAND: Graph Learning Framework for Dynamic Graphs (**KDD, 2022**) [[paper](https://arxiv.org/pdf/2208.07239.pdf)][[code](https://github.com/snap-stanford/roland)]

* 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)]

* 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)]

* 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)]

* 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)]

* 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)]

* 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)]

* 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)]

* A Viral Marketing-Based Model For Opinion Dynamics in Online Social Networks (**WWW, 2022**) [[paper](https://arxiv.org/pdf/2202.03573.pdf)]

* 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)]

* 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)]

* Few-shot Link Prediction in Dynamic Networks (**WSDM, 2022**) [[paper](http://www.shichuan.org/doc/120.pdf)]

* On Generalizing Static Node Embedding to Dynamic Settings (**WSDM, 2022**) [[paper](https://gemslab.github.io/papers/dijin-2021-trg.pdf)]

* 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)]

* DA-Net: Distributed Attention Network for Temporal Knowledge Graph Reasoning (**CIKM, 2022**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3511808.3557280)]

* A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning (**CIKM, 2022**) [[paper](https://arxiv.org/pdf/2208.14073.pdf)]

* Dynamic Hypergraph Learning for Collaborative Filtering (**CIKM, 2022**) [[paper]](https://dl.acm.org/doi/pdf/10.1145/3511808.3557301)

### 2021

* 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)]

* 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)]

* Subset Node Representation Learning over Large Dynamic Graphs (**KDD, 2021**) [[paper](https://arxiv.org/pdf/2106.01570.pdf)][[code](https://github.com/zjlxgxz/DynamicPPE)]

* 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)]

* Learning to Walk across Time for Temporal Knowledge Graph Completion (**KDD, 2021**) [[paper](https://arxiv.org/pdf/2012.10595v1.pdf)]

* Forecasting Interaction Order on Temporal Graphs (**KDD, 2021**) 

* 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)]

* Inductive Representation Learning in Temporal Networks via Mining Neighborhood and Community Influences (**SIGIR, 2021**)

* TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion [[paper](https://arxiv.org/pdf/2104.08419.pdf)]

* 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)]

* Temporal Augmented Graph Neural Networks for Session-Based Recommendations (**SIGIR SHORT, 2021**) [[paper](https://www4.comp.polyu.edu.hk/~xiaohuang/docs/Huachi_sigir2021.pdf)]

* 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)]

* TEDIC: Neural Modeling of Behavioral Patterns in Dynamic Social Interaction Networks (**WWW, 2021**) [[paper](http://snap.stanford.edu/tedic/files/www21_tedic.pdf)]

* Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs (**AAAI, 2021**) [[paper](https://arxiv.org/pdf/2104.02228.pdf)]

* 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)]

* Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay (**AAAI, 2021**) [[paper](https://arxiv.org/pdf/2003.09908.pdf)]

* Learning and Updating Node Embedding on Dynamic Heterogeneous Information Network (**WSDM, 2021**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3437963.3441745)]

* 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)]

* Cache-based GNN System for Dynamic Graphs (**CIKM 2021**) [[paper]]

* Self-supervised Representation Learning on Dynamic Graphs (**CIKM 2021**)[[paper]]

* Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer [[paper](https://arxiv.org/pdf/2108.06625.pdf)][[code](https://github.com/DyGRec/TGSRec)]

* Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs (**CIKM 2021**) [[paper](https://arxiv.org/pdf/2005.07427.pdf)]

* Neural Higher-order Pattern (Motif) Prediction in Temporal Networks (**ARXIV, 2021**) [[paper](https://arxiv.org/pdf/2106.06039.pdf)]

### 2020

* 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)]

* 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)]

* 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)]

* 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)]

* 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)]

* 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/)]

* Heterogeneous Graph Transformer (**WWW, 2020**) [[paper](https://arxiv.org/pdf/2003.01332.pdf)][[code](https://github.com/acbull/pyHGT)]

* Streaming Graph Neural Network (**SIGIR, 2020**) [[paper](https://arxiv.org/pdf/1810.10627.pdf)][[code](https://github.com/alge24/DyGNN)]

* 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)]

* Temporal Network Embedding with High-Order Nonlinear Information (**AAAI, 2020**) [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/5993)]

* Motif-Preserving Temporal Network Embedding (**IJCAI, 2020**) [[paper](https://www.ijcai.org/proceedings/2020/0172.pdf)]

* Dynamic Graph Collaborative Filtering (**ICDM, 2020**) [[paper](https://arxiv.org/pdf/2101.02844.pdf)][[code](https://github.com/CRIPAC-DIG/DGCF)]

* 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)]

* Learning and Updating Node Embedding on Dynamic Heterogeneous Information Network (**WSDM, 2020**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3437963.3441745)][[code]()]

* Continuous-Time Dynamic Graph Learning via Neural Interaction Processes (**CIKM, 2020**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3340531.3411946)]

* 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)]

* Streaming Graph Neural Network via Continue Learning (**CIKM, 2020**) [[paper](https://arxiv.org/pdf/2009.10951.pdf)][[code](https://github.com/Junshan-Wang/ContinualGNN)]

* Disentangle-based Continual Graph Representation Learning (**EMNLP, 2020**) [[paper](https://arxiv.org/pdf/2010.02565.pdf)][[code](https://github.com/KXY-PUBLIC/DiCGRL)]

* 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)]

* 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)]

* EPNE: Evolutionary Pattern Preserving Network Embedding (**ECAI, 2020**) [[paper](http://ecai2020.eu/papers/528_paper.pdf)]

* GloDyNE: Global Topology Preserving Dynamic Network Embedding (**TKDE, 2020**) [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9302718)][[code](https://github.com/houchengbin/GloDyNE)]

* 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)]

* Lifelong Graph Learning (**ARXIV, 2020**) [[paper](https://arxiv.org/pdf/2009.00647.pdf)]

### 2019

* Variational Graph Recurrent Neural Networks (**NeurIPS, 2019**) [[paper](https://papers.nips.cc/paper/2019/file/a6b8deb7798e7532ade2a8934477d3ce-Paper.pdf)][[code](https://github.com/VGraphRNN/VGRNN)]

* 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)]

* DyRep: Learning Representations over Dynamic Graphs (**ICLR, 2019**) [[paper](https://openreview.net/pdf?id=HyePrhR5KX)]

* Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks (**KDD, 2019**) [[paper](https://arxiv.org/pdf/1908.01207.pdf)][[code](https://github.com/srijankr/jodie)]

* 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)]

* EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs (**AAAI, 2019**) [[paper](https://arxiv.org/pdf/1902.10191.pdf)][[code](https://github.com/IBM/EvolveGCN)]

* Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems (**WWW, 2019**) [[paper](https://arxiv.org/pdf/1904.04381.pdf)]

* Real-Time Streaming Graph Embedding Through Local Actions (**WWW, 2019**) [[paper](https://nickduffield.net/download/papers/DL4G-SDE-2019.pdf)]

* 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)]

* 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.)]

* Temporal Network Embedding with Micro- and Macro-dynamics (**CIKM, 2019**) [[paper](https://par.nsf.gov/servlets/purl/10148548)][[code](https://github.com/rootlu/MMDNE)]

### 2018

* 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)]

* Embedding Temporal Network via Neighborhood Formation (**KDD, 2018**) [[paper](https://dl.acm.org/doi/pdf/10.1145/3219819.3220054)][[code]()]

* 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)]

* 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)]

* Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embedding (**IJCAI, 2018**) [[paper](https://www.ijcai.org/proceedings/2018/0288.pdf)]

### 2017

* 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)]

* 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]()]

* 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)]

## Tools

### General Graph Learning

* [Deep Graph Library](https://www.dgl.ai/)

* [Pytorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/)

* [Pytorch Geometric Temporal](https://pytorch-geometric-temporal.readthedocs.io/en/latest/notes/introduction.html)

* [Stellar Graph](https://stellargraph.readthedocs.io/en/stable/)

* [GraphVite](https://graphvite.io/)

### Knowledge Graph

* [DGL-KE](https://dglke.dgl.ai/doc/)

* [OpenKE](https://github.com/thunlp/OpenKE)

### Recommender System

* [RecBole](https://www.recbole.io/)