本项目用于整理异质图神经网络（HGNN）相关数据、算法、其他资料。
非使用GNN方法的异质图相关工作（尤其是推荐系统和知识图谱相关工作）暂不列入。暂时主要考虑创新点在GNN方面的（如NLP领域主要创新点在如何建图方面的，暂不列入）。
作者主要用PyTorch和PyG。
超过5M的文件都存储在了百度网盘上，以方便大陆用户下载：
链接：https://pan.baidu.com/s/1P1X9LjT85DU9OBPAr6l3bg
提取码：ea7u
简单介绍：
| 数据集名称 | 下载和预处理方式 | 出处 | 常用任务 |
|---|---|---|---|
| ogbn-mag | PyG OGM_MAG ogb |
MAG数据集的子集 | 节点分类 |
| MAG | 已停用 | Microsoft academic graph: When experts are not enough. | |
| AMiner (metapath2vec) | PyG（百度网盘） | metapath2vec: Scalable Representation Learning for Heterogeneous Networks | 节点分类 |
| AMinerNetwork | https://www.aminer.org/aminernetwork | A multilayered approach for link prediction in heterogeneous complex networks | 链路预测 |
| DBIS | https://ericdongyx.github.io/metapath2vec/m2v.html （百度网盘和HGNN_Collection/load_data/dbis_pyg.py） | metapath2vec: Scalable Representation Learning for Heterogeneous Networks | 节点相似度 |
| DBLP (MAGNN) | PyG (DBLP和HGBDataset)（百度网盘） | MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding | 节点分类 |
| IMDB (MAGNN) | PyG（百度网盘） | MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding | 节点分类 |
| IMDB (Simple-HGN) | PyG HGBDataset | Are We Really Making Much Progress? Revisiting, Benchmarking, and Refining Heterogeneous Graph Neural Networks | 节点分类 |
| LastFM | PyG（百度网盘） | MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding | 链路预测 |
| MovieLens (PyG) | PyG | https://movielens.org/ | |
| ACM | PyG HGBDataset | Are We Really Making Much Progress? Revisiting, Benchmarking, and Refining Heterogeneous Graph Neural Networks | 节点分类 |
| Freebase | PyG HGBDataset | Are We Really Making Much Progress? Revisiting, Benchmarking, and Refining Heterogeneous Graph Neural Networks | 节点分类 |
| ogbl-biokg | https://ogb.stanford.edu/docs/linkprop/ | 链路预测 |
详细介绍：
《HGNN图数据集》
相关介绍博文： 异质图神经网络（HGNN）常用数据集信息统计（持续更新ing...）_诸神缄默不语的博客-CSDN博客_异质图数据集 PyG (PyTorch Geometric) Dropbox系图数据集无法下载的解决方案（AMiner, DBLP, IMDB, LastFM）（持续更新ing...）
2024年
KG嵌入：
2023年
通用节点嵌入：
- (WWW) A Post-Training Framework for Improving Heterogeneous Graph Neural Networks
- (Journal of Computational Science) Multi-view contrastive learning for multilayer network embedding
- (Entropy) Unsupervised Embedding Learning for Large-Scale Heterogeneous Networks Based on Metapath Graph Sampling
- (Scientific Reports) A multi-view contrastive learning for heterogeneous network embedding
- HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformer
- Heterophily-Aware Graph Attention Network
动态图节点嵌入：
- (Neurocomputing) Dynamic heterogeneous graph representation learning with neighborhood type modeling
graph rewiring:
- (WWW) Homophily-oriented Heterogeneous Graph Rewiring：同配性+异质性
graphlet和orbit计数：
交叉学科：
- (Phys. Rev. E) Deterministic, quenched and annealed parameter estimation for heterogeneous network models：计量经济学+物理
2022年
综述：
- (Artificial Intelligence Review) Heterogeneous graph neural networks analysis: a survey of techniques, evaluations and applications
通用节点嵌入：
- (World Wide Web) HGNN-ETA: Heterogeneous graph neural network enriched with text attribute：利用节点的文本特征信息
- (World Wide Web) How the four-nodes motifs work in heterogeneous node representation?
- (CIKM) SplitGNN: Splitting GNN for Node Classification with Heterogeneous Attention：联邦学习
- (SDM) Structure-Enhanced Heterogeneous Graph Contrastive Learning：提出STENCIL模型，跨视图（保证视图之间一致性最大化）（基于metapaths构建视图（metapath实例起终点构成的同质图），最大化同一节点在不同视图上嵌入的相似性，将各视图的嵌入attentively聚合）+对比学习+结构嵌入
- (Transactions on Big Data) A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources：综述
- (Knowledge-Based Systems) Megnn: Meta-path extracted graph neural network for heterogeneous graph representation learning：自动提取metapaths+可解释性
- (IEEE Transactions on Knowledge and Data Engineering) Heterogeneous Graph Representation Learning with Relation Awareness：提出R-HGNN模型
- (IEEE Transactions on Knowledge and Data Engineering) mg2vec: Learning Relationship-Preserving Heterogeneous Graph Representations via Metagraph Embedding：meta graph
- (IEEE Transactions on Knowledge and Data Engineering) RHINE: Relation Structure-Aware Heterogeneous Information Network Embedding
- (IEEE Transactions on Knowledge and Data Engineering) Explicit Message-Passing Heterogeneous Graph Neural Network：提出EMP模型。本文认为传统用metapaths将异质图构建为同质图的方法是隐式的，而EMP模型显式信息传递
- (南洋理工大学硕士论文) Contrastive learning for heterogeneous graph neural networks
- (Neural Networks) Latent neighborhood-based heterogeneous graph representation：随机游走增强邻居+GTN学习metapaths
- (IEEE Transactions on Neural Networks and Learning Systems) Learning Knowledge Graph Embedding With Heterogeneous Relation Attention Networks
- (Appl. Sci.) MBHAN: Motif-Based Heterogeneous Graph Attention Network：基于motif
- (Data Mining and Knowledge Discovery) Personalised meta-path generation for heterogeneous graph neural networks：提出PM-HGNN模型，强化学习（将找metapaths视作马尔科夫决策过程）
- (Machine Learning) Heterogeneous graph embedding with single-level aggregation and infomax encoding：提出无监督HGNN模型HIME，对节点特征应用MLP（每一种节点用一个模型），直接进行聚合。损失函数鼓励邻居相近+信息最大化
- (International Journal of Machine Learning and Cybernetics) Multiple heterogeneous network representation learning based on multi-granularity fusion：将结构（一阶邻居）和语义（meta-path）视作不同粒度，结合来学习表征
- (Journal of Applied Mathematics) Classification Algorithm for Heterogeneous Network Data Streams Based on Big Data Active Learning
- (IEEE Access) Siamese Network Based Multiscale Self-Supervised Heterogeneous Graph Representation Learning：提出SNMH模型，自监督学习+对比学习（metapaths和one-hop）+孪生神经网络
- (ICCWAMTIP) Relation Heterogeneous Graph Neural Network
- (ICDMW) Graph Convolutional Neural Network based on the Combination of Multiple Heterogeneous Graphs
- Simple and Efficient Heterogeneous Graph Neural Network：提出SeHGNN模型，预处理+无参+轻量级
- Relation Embedding based Graph Neural Networks for Handling Heterogeneous Graph：不用metapaths
- Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks：关注过平滑问题
- Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning：提出HGCL模型，自监督学习+对比学习（节点属性和拓扑结构）
- Heterogeneous Graph Contrastive Multi-view Learning：提出HGCML模型，关注减轻对比学习（metapaths）中的采样偏差
- Heterogeneous Graph Masked Autoencoders：提出HGMAE模型，auto encoder
- Meta-node: A Concise Approach to Effectively Learn Complex Relationships in Heterogeneous Graphs：不用预定义的meta-paths或meta-graphs
预训练：
multiplex graph：
- (KDD) Multiplex Heterogeneous Graph Convolutional Network
- (ijtr) DSMN: A New Approach for Link Prediction in Multilplex Networks
小样本学习：
- (KDD) Few-shot Heterogeneous Graph Learning via Cross-domain Knowledge Transfer
AI安全：
- (AAAI) Robust Heterogeneous Graph Neural Networks against Adversarial Attacks
知识蒸馏：
- (WWW) Collaborative Knowledge Distillation for Heterogeneous Information Network Embedding
- (Neurocomputing) HIRE: Distilling high-order relational knowledge from heterogeneous graph neural networks
匹配节点：
- (AAAI) From One to All: Learning to Match Heterogeneous and Partially Overlapped Graphs
链路预测：
2021年
通用节点嵌入：
- (KDD) Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks：先把近年多篇论文（HAN、GTN、RSHN、HetGNN、MAGNN、HGT、HetSANN、RGCN、GATNE、KGCN、KAGT）喷了一遍，然后提出Simple-HGN模型（参考博文：Re10：读论文 Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous gr_诸神缄默不语的博客-CSDN博客）
- (KDD) Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning：提出HeCo模型，跨视图（metapath和network）+对比学习+自监督学习
- (KDD) HGK-GNN: Heterogeneous Graph Kernel based Graph Neural Networks：引入HGK
- (KDD) DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks：NAS
- (AAAI) Heterogeneous graph structure learning for graph neural networks
- (WWW) Heterogeneous Graph Neural Network via Attribute Completion：提出HGNN-AC模型，端到端地同时学习节点特征补全和GNN学习
- (IJCAI) Heterogeneous Graph Information Bottleneck：提出HGIB模型，无监督学习+信息论
- (ECML PKDD) Multi-view Self-supervised Heterogeneous Graph Embedding：提出MVSE模型，用基于metapaths的多视图实现自监督学习
- (IEEE Transactions on Knowledge and Data Engineering) Heterogeneous Graph Propagation Network：提出HPN模型，缓解HGNN中的confusion phenomenon问题（类似同质GNN中的过平滑问题）
- (IEEE Transactions on Knowledge and Data Engineering) Interpretable and Efficient Heterogeneous Graph Convolutional Network：提出ie-HGCN模型，可以广泛评估各种metapaths
- (IEEE Transactions on Knowledge and Data Engineering) Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks：提出HAE模型，使用了meta-graphs
- (IEEE Transactions on Knowledge and Data Engineering) HGATE: Heterogeneous Graph Attention Auto-Encoders：无监督学习+auto encoder
- (IEEE Transactions on Knowledge and Data Engineering) Walking with Attention: Self-guided Walking for Heterogeneous Graph Embedding：提出SILK模型，基于随机游走
- (Big Data Mining and Analytics) Attention-aware heterogeneous graph neural network：提出AHNN模型，metapath+attention
- (ICME) Revisiting Graph Neural Networks for Node Classification in Heterogeneous Graphs：关注过拟合问题
- (ICLR 2022被拒+撤回投稿) R-GSN: The Relation-based Graph Similar Network for Heterogeneous Graph：不用metapaths，据称在ogbn-mag图上达到了SOTA效果
动态图：
- (ECML PKDD) Dynamic Heterogeneous Graph Embedding via Heterogeneous Hawkes Process：霍克斯过程
超图：
- (WSDM) Heterogeneous Hypergraph Embedding for Graph Classification
社区检测：
- (CIKM) Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model：提出CP-GNN模型
图生成：
- (ICDM) Deep Generation of Heterogeneous Networks：提出HGEN模型
2020年
通用节点嵌入：
- (IJCAI) Heterogeneous Network Representation Learning：综述
- (AAAI) An Attention-based Graph Neural Network for Heterogeneous Structural Learning：提出HetSANN模型，基于关系类型，建立attention机制，实现节点信息聚合，不使用metapath（参考博文：Re22：读论文 HetSANN An Attention-based Graph Neural Network for Heterogeneous Structural Learning_诸神缄默不语的博客-CSDN博客）
- (WWW) MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding：首先对节点特征进行转换，然后聚合metapath内部信息，然后聚合各metapath的信息
- (WWW) Heterogeneous Graph Transformer：提出HGT模型，把整个Transformer结构改到图上，这种感觉
- Hybrid Micro/Macro Level Convolution for Heterogeneous Graph Learning：提出HGConv模型
- Reinforcement Learning Enhanced Heterogeneous Graph Neural Network：提出HGRL模型，强化学习（将找metapaths视作马尔科夫决策过程）
- (被ICLR 2021拒了) Scalable Graph Neural Networks for Heterogeneous Graphs：提出NARS模型，关注scalability问题
动态图：
- (ECIR) Dynamic Heterogeneous Graph Embedding Using Hierarchical Attentions：提出DyHAN模型（实验仅做了链路预测）
- (ICKG) Heterogeneous Dynamic Graph Attention Network：提出HDGAN模型
- Meta Graph Attention on Heterogeneous Graph with Node-Edge Co-evolution：提出CoMGNN和ST-CoMGNN模型
multiplex graph：
- (AAAI) Unsupervised Attributed Multiplex Network Embedding：提出DMGI模型
多模态：
- (KDD) HGMF: Heterogeneous Graph-based Fusion for Multimodal Data with Incompleteness
anchor link prediction任务：
- (AAAI) Type-Aware Anchor Link Prediction across Heterogeneous Networks Based on Graph Attention Network
2019年
通用节点嵌入：
- (WWW) Heterogeneous Graph Attention Network：提出HAN模型，先基于metapath attentively聚合节点信息，然后attentively聚合metapath信息
- (NeurIPS) Graph Transformer Networks：提出GTN模型，自动学习metapaths
- (KDD) Heterogeneous Graph Neural Network：提出HetGNN模型，用RWR抽样异质邻居，按节点类型分类，然后用聚合
- (KDD) Representation Learning for Attributed Multiplex Heterogeneous Network：提出GATNE模型
- (KDD) Adversarial Learning on Heterogeneous Information Networks：提出HetGAN模型
- (ICDM) Relation Structure-Aware Heterogeneous Graph Neural Network：提出RSHN模型，用coarsened line graph先获得边特征，然后传播节点和边特征
- (CIKM) BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network：提出BHIN2vec模型，随机游走+skip gram+多任务，解决HIN中不同种类边数不平衡的问题
- Heterogeneous Deep Graph Infomax：提出HDGI模型，信息论+无监督学习
图匹配：
- (IJCAI) Heterogeneous Graph Matching Networks for Unknown Malware Detection
底层编译技术：
- (PACT) Gluon-Async: A Bulk-Asynchronous System for Distributed and Heterogeneous Graph Analytics
2018年
通用节点嵌入：
- (CIKM) Are Meta-Paths Necessary?: Revisiting Heterogeneous Graph Embeddings
- (KDD) Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks
- (TKDE) Heterogeneous Information Network Embedding for Recommendation：提出HERec模型，将异质图转换为metapath-based graphs，用skip-gram嵌入
- (ESWC) Modeling Relational Data with Graph Convolutional Networks：提出RGCN模型
- (SIAM) AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks
链路预测：
2017年
通用节点嵌入：
- (KDD) metapath2vec: Scalable Representation Learning for Heterogeneous Networks：用基于metapath的随机游走来构建邻居，然后用类似word2vec的逻辑来实现节点表征（类似DeepWalk）（参考博文：Re31：读论文 metapath2vec: Scalable Representation Learning for Heterogeneous Networks_诸神缄默不语的博客-CSDN博客）
- (KDD) Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks：提出ESim模型，根据预定义的权重从抽样出的metapaths实例中捕获节点语义
- (CIKM) HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning
- (WSDM) Embedding of Embedding (EOE): Joint Embedding for Coupled Heterogeneous Networks
2015年
2014年
- (KDD) Mining heterogeneous information networks: a structural analysis approach
- (CIKM) Meta-Path-Based Ranking with Pseudo Relevance Feedback on Heterogeneous Graph for Citation Recommendation
2013年
博弈论：
- (PLoS ONE) Evolution of Cooperation in a Heterogeneous Graph: Fixation Probabilities under Weak Selection
2011年
节点分类：
- (World Wide Web) Graffiti: graph-based classification in heterogeneous networks
节点相似性：
- (Proceedings of the VLDB Endowment) PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks
2010年
通用节点嵌入：
- (ECML PKDD) Graph Regularized Transductive Classification on Heterogeneous Information Networks
- Optimal Embedding of Heterogeneous Graph Data with Edge Crossing Constraints