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Graph Learning Notes (In Chinese)

在此整理了一些个人的文献阅读笔记,主要是图学习领域的,希望大家多多指正。

Survey

  • Graph self-supervised learning: A survey (TKDE 2022) [paper][note]

  • 面向社会计算的网络表示学习 (清华博士论文 2018) [paper][note]

  • A Survey on Network Embedding (AAAI 2017) [paper][note]

  • 网络表示学习专题 (CCF 2017) [note]

Paper

2023

  • Heterogeneous Graph Learning for Acoustic Event Classification (ICASSP) [paper][code][note]

2022

  • Neighborhood-aware Scalable Temporal Network Representation Learning (LOG) [paper][code]note]

  • GraphMAE: Self-Supervised Masked Graph Autoencoders (KDD) [paper][code]note]

  • ROLAND: Graph Learning Framework for Dynamic Graphs (KDD) [paper][code][note]

  • SAIL: Self Augmented Graph Contrastive Learning (AAAI) [paper][note]

  • TREND: TempoRal Event and Node Dynamics for Graph Representation Learning (WWW) [paper][code][note]

  • CGC: Contrastive Graph Clustering for Community Detection and Tracking (WWW) [paper][note]

  • Pre-Training on Dynamic Graph Neural Networks (Neurocomputing) [paper][note]

2021

  • Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer (CIKM) [paper][code][note]

  • Do Transformers Really Perform Bad for Graph Representation (NeurIPS) [paper][code][note]

  • Structural Deep Clustering Network (WWW) [paper][code][note]

  • Deep Fusion Clustering Network (AAAI) [paper][code][note]

  • Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks (ICLR) [paper][code][note]

  • Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay (AAAI) [paper][note]

  • Combining Label Propagation and Simple Models Out-performs Graph Neural Networks (ICLR) [paper][code][note]

  • Accurate Learning of Graph Representations with Graph Multiset Pooling (ICLR) [paper][code][note]

  • Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting (AAAI Best Paper) [paper][code][note]

  • Self-supervised Graph Learning for Recommendation (SIGIR) [paper][code][note]

  • Learnable Embedding Sizes for Recommender Systems (ICLR) [paper][code][note]

  • Adversarial Directed Graph Embedding (AAAI) [paper][code][note]

  • Graph Game Embedding (AAAI) [paper][note]

  • Towards Robust Graph Contrastive Learning (WWW Workshop) [paper][note]

  • Towards open-world feature extrapolation: An inductive graph learning approach (NeurIPS) [paper][note]

  • Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis (ACL) [paper][code][note]

  • How to Find Your Friendly Neighborhood: Graph Attention Design with Self-supervision (ICLR) [paper][code][note]

2020

  • Contrastive Multi-View Representation Learning on Graphs (ICML) [paper][code][note]

  • Temporal Graph Networks for Deep Learning on Dynamic Graphs (ICML Workshop) [paper][code][note]

  • Inductive representation learning on temporal graphs (ICLR) [paper][code][note]

  • EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graph (AAAI) [paper][code][note]

  • DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks (WSDM) [paper][code][note]

  • Inductive and Unsupervised Representation Learning on Graph Structured Objects (ICLR) [paper][note]

  • Continuous-Time Dynamic Graph Learning via Neural Interaction Processes (CIKM) [note]

  • GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training (KDD) [paper][code][note]

  • JNET: Learning User Representations via Joint Network Embedding and Topic Embedding (WSDM) [paper][code][note]

  • Deep Graph Contrastive Representation Learning (ICML Workshop) [paper][code][note]

  • On the equivalence between positional node embeddings and structural graph representations (ICLR) [paper][note]

  • Explain Graph Neural Networks to Understand Weight Graph Features (IFIP) [paper][note]

2019

  • DyREP: Learing Representations over Dynamic Graphs (ICLR) [paper][note]

  • Self-attention with Functional Time Representation Learning (NeurIPS) [paper][code][note]

  • Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks (KDD) [paper][code][slide][note]

  • Node Embedding over Temporal Graphs (IJCAI) [paper][code][note]

  • Spatio-Temporal Attentive RNN for Node Classification in Temporal Attributed Graph (IJCAI) [paper][code][note]

  • GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding (WWW) [paper][code][note]

2018

  • Continuous-Time Dynamic Network Embeddings (WWW) [paper][code][note]

  • Embedding Temporal Network via Neighborhood Formation (KDD) [paper][note]

  • Learning dynamic embeddings from temporal interactions (arXiv) [paper][note]

  • Arbitrary-Order Proximity Preserved Network Embedding (KDD) [paper][code][note]

  • A Unified Framework for Community Detection and Network Representation Learning (TKDE) [paper][note]

2017

  • CANE: Context-Aware Network Embedding for Relation Modeling (ACL) [paper][code][slide][note]

  • Inductive representation learning on large graph (NeurIPS) [paper][code][note]

  • PRISM: Profession Identification in Social Media (ACM) [paper][note]

  • TransNet: Translation-Based NRL for Social Relation Extraction (IJCAI) [paper][code][slide][note]

  • Learning Community Embedding with Community Detection and Node Embedding on Graphs (CIKM) [paper][code][note]

2016

  • Asymmetric Transitivity Preserving Graph Embedding (KDD) [paper][code][note]

  • Structural Deep Network Embedding (KDD) [paper][code][note]

  • node2vec: Scalable Feature Learning for Networks (KDD) [paper][code][note]

  • Max-Margin DeepWalk: Discriminative Learning of Network Representation (IJCAI) [paper][code][note]

2015

  • LINE: Large-scale Information Network Embedding (WWW) [paper][code][note]

2014

  • DeepWalk: online learning of social representations (KDD) [paper][code][note]

Cite Us

如果您感觉有所帮助,请引用我们的文章作为鼓励~

@inproceedings{TGC_ML_ICLR,
  title={Deep Temporal Graph Clustering},
  author={Liu, Meng and Liu, Yue and Liang, Ke and Tu, Wenxuan and Wang, Siwei and Zhou, Sihang and Liu, Xinwang},
  booktitle={The 12th International Conference on Learning Representations},
  year={2024}
}

@article{S2T_ML,
  title={Self-Supervised Temporal Graph Learning with Temporal and Structural Intensity Alignment},
  author={Liu, Meng and Liang, Ke and Zhao, Yawei and Tu, Wenxuan and Zhou, Sihang and Liu, Xinwang and He Kunlun},
  journal={arXiv preprint arXiv:2302.07491},
  year={2023}
}