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。
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Neighborhood-aware Scalable Temporal Network Representation Learning (LOG) [paper][code]note]
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GraphMAE: Self-Supervised Masked Graph Autoencoders (KDD) [paper][code]note]
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ROLAND: Graph Learning Framework for Dynamic Graphs (KDD) [paper][code][note]
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SAIL: Self Augmented Graph Contrastive Learning (AAAI) [paper][note]
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TREND: TempoRal Event and Node Dynamics for Graph Representation Learning (WWW) [paper][code][note]
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CGC: Contrastive Graph Clustering for Community Detection and Tracking (WWW) [paper][note]
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Pre-Training on Dynamic Graph Neural Networks (Neurocomputing) [paper][note]
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Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer (CIKM) [paper][code][note]
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Do Transformers Really Perform Bad for Graph Representation (NeurIPS) [paper][code][note]
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Structural Deep Clustering Network (WWW) [paper][code][note]
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Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks (ICLR) [paper][code][note]
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Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay (AAAI) [paper][note]
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Combining Label Propagation and Simple Models Out-performs Graph Neural Networks (ICLR) [paper][code][note]
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Accurate Learning of Graph Representations with Graph Multiset Pooling (ICLR) [paper][code][note]
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Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting (AAAI Best Paper) [paper][code][note]
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Self-supervised Graph Learning for Recommendation (SIGIR) [paper][code][note]
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Learnable Embedding Sizes for Recommender Systems (ICLR) [paper][code][note]
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Adversarial Directed Graph Embedding (AAAI) [paper][code][note]
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Towards Robust Graph Contrastive Learning (WWW Workshop) [paper][note]
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Towards open-world feature extrapolation: An inductive graph learning approach (NeurIPS) [paper][note]
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Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis (ACL) [paper][code][note]
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How to Find Your Friendly Neighborhood: Graph Attention Design with Self-supervision (ICLR) [paper][code][note]
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Contrastive Multi-View Representation Learning on Graphs (ICML) [paper][code][note]
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Temporal Graph Networks for Deep Learning on Dynamic Graphs (ICML Workshop) [paper][code][note]
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Inductive representation learning on temporal graphs (ICLR) [paper][code][note]
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EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graph (AAAI) [paper][code][note]
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DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks (WSDM) [paper][code][note]
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Inductive and Unsupervised Representation Learning on Graph Structured Objects (ICLR) [paper][note]
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Continuous-Time Dynamic Graph Learning via Neural Interaction Processes (CIKM) [note]
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GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training (KDD) [paper][code][note]
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JNET: Learning User Representations via Joint Network Embedding and Topic Embedding (WSDM) [paper][code][note]
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Deep Graph Contrastive Representation Learning (ICML Workshop) [paper][code][note]
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On the equivalence between positional node embeddings and structural graph representations (ICLR) [paper][note]
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Explain Graph Neural Networks to Understand Weight Graph Features (IFIP) [paper][note]
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DyREP: Learing Representations over Dynamic Graphs (ICLR) [paper][note]
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Self-attention with Functional Time Representation Learning (NeurIPS) [paper][code][note]
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Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks (KDD) [paper][code][slide][note]
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Node Embedding over Temporal Graphs (IJCAI) [paper][code][note]
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Spatio-Temporal Attentive RNN for Node Classification in Temporal Attributed Graph (IJCAI) [paper][code][note]
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GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding (WWW) [paper][code][note]
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Continuous-Time Dynamic Network Embeddings (WWW) [paper][code][note]
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Embedding Temporal Network via Neighborhood Formation (KDD) [paper][note]
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Learning dynamic embeddings from temporal interactions (arXiv) [paper][note]
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Arbitrary-Order Proximity Preserved Network Embedding (KDD) [paper][code][note]
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A Unified Framework for Community Detection and Network Representation Learning (TKDE) [paper][note]
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CANE: Context-Aware Network Embedding for Relation Modeling (ACL) [paper][code][slide][note]
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Inductive representation learning on large graph (NeurIPS) [paper][code][note]
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PRISM: Profession Identification in Social Media (ACM) [paper][note]
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TransNet: Translation-Based NRL for Social Relation Extraction (IJCAI) [paper][code][slide][note]
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Learning Community Embedding with Community Detection and Node Embedding on Graphs (CIKM) [paper][code][note]
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Asymmetric Transitivity Preserving Graph Embedding (KDD) [paper][code][note]
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node2vec: Scalable Feature Learning for Networks (KDD) [paper][code][note]
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Max-Margin DeepWalk: Discriminative Learning of Network Representation (IJCAI) [paper][code][note]
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}
}