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 (**https://arxiv.org/pdf/2203.14303.pdff/2203.14303.pdf)][code][note]

• CGC: Contrastive Graph Clustering for Community Detection and Tracking (**https://dl.acm.org/doi/abs/10.1145/3485447.3512160/3485447.3512160)][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 (**https://arxiv.org/pdf/2002.01633.pdff/2002.01633.pdf)][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 (**https://arxiv.org/pdf/2102.13085.pdfiv.org/pdf/2102.13085.pdf)][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 (**https://arxiv.org/pdf/1903.00757.pdff/1903.00757.pdf)][code][note]

2018

• Continuous-Time Dynamic Network Embeddings (**https://dl.acm.org/doi/pdf/10.1145/3184558.3191526/3184558.3191526)][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 (**https://arxiv.org/pdf/1503.03578.pdf%C2%A0%E3%80%90WWW2%A0%E3%80%90WWW)][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}
}