/Awesome-DynamicGraphLearning

Awesome papers about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs).

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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]
  • Graph Neural Networks Designed for Different Graph Types: A Survey (ARXIV, 2022) [paper]
  • Representation Learning for Dynamic Graphs: A Survey (JMLR, 2020) [paper]
  • A Survey on Embedding Dynamic Graphs (ARXIV, 2021) [paper]
  • Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (ARXIV, 2019) [paper]
  • Nonlinearity + Networks: A 2020 Vision (ARXIV, 2019) [paper]
  • Temporal networks (Physics Report, 2012) [paper]

Papers

2023

  • Towards Better Dynamic Graph Learning: New Architecture and Unified Library (ARXIV, 2023) [paper][code]
  • SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning (ARXIV, 2023) [paper][code]
  • Towards Open Temporal Graph Neural Networks (ICLR, 2023) [paper][code]
  • Do We Really Need Complicated Model Architectures For Temporal Networks? (ICLR, 2023) [paper][code]
  • Zebra: When Temporal Graph Neural Networks Meet Temporal Personalized PageRank (VLDB, 2023) [paper][code]
  • Temporal SIR-GN: Eficient and Efective Structural Representation Learning for Temporal Graphs (VLDB, 2023) [paper][code]
  • SEIGN: A Simple and Efficient Graph Neural Network for Large Dynamic Graphs (ICDE, 2023) [paper]
  • A Higher-Order Temporal H-Index for Evolving Networks (KDD, 2023) [paper]
  • Using Motif Transitions for Temporal Graph Generation (KDD, 2023) [paper]
  • Temporal Dynamics Aware Adversarial Attacks on Discrete-Time Graph Models (KDD, 2023) [paper][code]
  • Fairness-Aware Continuous Predictions of Multiple Analytics Targets in Dynamic Networks (KDD, 2023) [paper]
  • DyTed: Disentangled Representation Learning for Discrete-time Dynamic Graph (KDD, 2023) [paper]
  • 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][code]
  • TIGER: Temporal Interaction Graph Embedding with Restarts (WWW, 2023) [paper][code]
  • HGWaveNet: A Hyperbolic Graph Neural Network for Temporal Link Prediction (WWW, 2023) [paper][code]
  • Expressive and Efficient Representation Learning for Ranking Links in Temporal Graphs (WWW, 2023) [paper][code]
  • Local Edge Dynamics and Opinion Polarization (WSDM, 2023) [paper][code]
  • Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs (WSDM, 2023) [paper][code]
  • Interpretable Research Interest Shift Detection with Temporal Heterogeneous Graphs (WSDM, 2023) [paper]
  • Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks (AAAI, 2023) [paper][code]

2022

  • TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs (VLDB, 2022) [paper][code]
  • Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs (Neurips, 2022)[paper][code]
  • Adaptive Data Augmentation on Temporal Graphs (Neurips, 2022) [paper]
  • Parameter-free Dynamic Graph Embedding for Link Prediction (Neurips, 2022) [paper][code]
  • Instant Graph Neural Networks for Dynamic Graphs (KDD, 2022) [paper][code]
  • Disentangled Dynamic Heterogeneous Graph Learning for Opioid Overdose Prediction (KDD, 2022) [paper][code]
  • ROLAND: Graph Learning Framework for Dynamic Graphs (KDD, 2022) [paper][code]
  • Subset Node Anomaly Tracking over Large Dynamic Graphs (KDD, 2022) [paper][code]
  • Streaming Graph Neural Networks via Generative Replay (KDD, 2022) [paper][code]
  • Neighborhood-aware Scalable Temporal Network Representation Learning (LoG, 2022) [paper][code]
  • DisenCTR: Dynamic Graph-based Disentangled Representation for Click-Through Rate Prediction (SIGIR, 2022) [paper][code]
  • STAM: A Spatiotemporal Aggregation Method for Graph Neural Network-based Recommendation (WWW, 2022) [paper][code]
  • Neural Predicting Higher-order Patterns in Temporal Networks (WWW, 2022) [paper][code]
  • TREND: TempoRal Event and Node Dynamics for Graph Representation Learning (WWW, 2022) [paper][code]
  • A Viral Marketing-Based Model For Opinion Dynamics in Online Social Networks (WWW, 2022) [paper]
  • EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs (WSDM, 2022) [paper][code]
  • Finding a Concise, Precise, and Exhaustive Set of Near Bi-Cliques in Dynamic Graphs (WSDM, 2022) [paper][code]
  • Few-shot Link Prediction in Dynamic Networks (WSDM, 2022) [paper]
  • On Generalizing Static Node Embedding to Dynamic Settings (WSDM, 2022) [paper]
  • Along the Time: Timeline-traced Embedding for Temporal Knowledge Graph Completion (CIKM, 2022) [paper][code]
  • DA-Net: Distributed Attention Network for Temporal Knowledge Graph Reasoning (CIKM, 2022) [paper]
  • A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning (CIKM, 2022) [paper]
  • Dynamic Hypergraph Learning for Collaborative Filtering (CIKM, 2022) [paper]

2021

  • Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks (ICLR, 2021) [paper][code]
  • Coupled Graph ODE for Learning Interacting System Dynamics (KDD, 2021) [paper][code]
  • Subset Node Representation Learning over Large Dynamic Graphs (KDD, 2021) [paper][code]
  • Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space [paper][code]
  • Learning to Walk across Time for Temporal Knowledge Graph Completion (KDD, 2021) [paper]
  • Forecasting Interaction Order on Temporal Graphs (KDD, 2021)
  • Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning (SIGIR, 2021) [paper][code]
  • 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]
  • SDG: A Simplified and Dynamic Graph Neural Network (SIGIR SHORT, 2021) [paper][code]
  • Temporal Augmented Graph Neural Networks for Session-Based Recommendations (SIGIR SHORT, 2021) [paper]
  • HINTS: Citation Time Series Prediction for New Publications via Dynamic Heterogeneous Information Network Embedding (WWW, 2021) [paper][code]
  • TEDIC: Neural Modeling of Behavioral Patterns in Dynamic Social Interaction Networks (WWW, 2021) [paper]
  • Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs (AAAI, 2021) [paper]
  • Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks (AAAI, 2021) [paper][code]
  • Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay (AAAI, 2021) [paper]
  • Learning and Updating Node Embedding on Dynamic Heterogeneous Information Network (WSDM, 2021) [paper]
  • F-FADE: Frequency Factorization for Anomaly Detection in Edge Streams (WSDM, 2021) [paper][code]
  • 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][code]
  • Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs (CIKM 2021) [paper]
  • Neural Higher-order Pattern (Motif) Prediction in Temporal Networks (ARXIV, 2021) [paper]

2020

  • Inductive Representation Learning on Temporal Graphs (ICLR, 2020) [paper][code]
  • Temporal Graph Networks for Deep Learning on Dynamic Graphs (ICML Workshop, 2020) [paper][code]
  • A Data-Driven Graph Generative Model for Temporal Interaction Networks (KDD, 2020) [paper][code]
  • Dynamic Knowledge Graph based Multi-Event Forecasting (KDD, 2020) [paper][code]
  • Laplacian Change Point Detection for Dynamic Graphs (KDD, 2020) [paper][code]
  • Algorithmic Aspects of Temporal Betweenness (KDD, 2020) [paper][code]
  • Heterogeneous Graph Transformer (WWW, 2020) [paper][code]
  • Streaming Graph Neural Network (SIGIR, 2020) [paper][code]
  • Next-item Recommendation with Sequential Hypergraphs (SIGIR, 2020) [paper][code]
  • Temporal Network Embedding with High-Order Nonlinear Information (AAAI, 2020) [paper]
  • Motif-Preserving Temporal Network Embedding (IJCAI, 2020) [paper]
  • Dynamic Graph Collaborative Filtering (ICDM, 2020) [paper][code]
  • DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks (WSDM, 2020) [papr][code]
  • Learning and Updating Node Embedding on Dynamic Heterogeneous Information Network (WSDM, 2020) [paper][code]
  • Continuous-Time Dynamic Graph Learning via Neural Interaction Processes (CIKM, 2020) [paper]
  • tdGraphEmbed: Temporal Dynamic Graph-Level Embedding (CIKM, 2020) [paper][code]
  • Streaming Graph Neural Network via Continue Learning (CIKM, 2020) [paper][code]
  • Disentangle-based Continual Graph Representation Learning (EMNLP, 2020) [paper][code]
  • TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion (EMNLP, 2020) [paper][code]
  • Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs (EMNLP, 2020) [paper][code]
  • EPNE: Evolutionary Pattern Preserving Network Embedding (ECAI, 2020) [paper]
  • GloDyNE: Global Topology Preserving Dynamic Network Embedding (TKDE, 2020) [paper][code]
  • Dynamic Heterogeneous Information Network Embedding with Meta-path based Proximity (TKDE, 2020) [paper][code]
  • Lifelong Graph Learning (ARXIV, 2020) [paper]

2019

  • Variational Graph Recurrent Neural Networks (NeurIPS, 2019) [paper][code]
  • Recurrent Space-time Graph Neural Networks (NeurIPS, 2019) [paper][code]
  • DyRep: Learning Representations over Dynamic Graphs (ICLR, 2019) [paper]
  • Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks (KDD, 2019) [paper][code]
  • Learning Dynamic Context Graphs for Predicting Social Events (KDD, 2019) [paper][code]
  • EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs (AAAI, 2019) [paper][code]
  • Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems (WWW, 2019) [paper]
  • Real-Time Streaming Graph Embedding Through Local Actions (WWW, 2019) [paper]
  • Dynamic Hypergraph Neural Networks (IJCAI, 2019) [paper][code]
  • Node Embedding over Temporal Graphs (IJCAI, 2019) [paper][code]
  • Temporal Network Embedding with Micro- and Macro-dynamics (CIKM, 2019) [paper][code]

2018

  • NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks (KDD, 2018) [paper][code]
  • Embedding Temporal Network via Neighborhood Formation (KDD, 2018) [paper][code]
  • Dynamic Network Embedding by Modeling Triadic Closure Process (AAAI, 2018) [paper][code]
  • Continuous-Time Dynamic Network Embeddings (WWW, 2018) [paper][code]
  • Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embedding (IJCAI, 2018) [paper]

2017

  • Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs (ICML, 2017) [paper][code]
  • The Co-Evolution Model for Social Network Evolving and Opinion Migration (KDD, 2017) [papercode]
  • Attributed Network Embedding for Learning in a Dynamic Environment (CIKM, 2017) [paper][code]

Tools

General Graph Learning

Knowledge Graph

Recommender System