1. Survey

• Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2018). How powerful are graph neural networks?. arXiv preprint arXiv:1810.00826. [paper] [paper_review]
• Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Philip, S. Y. (2020). A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, 32(1), 4-24. [paper]
• Cai, H., Zheng, V. W., & Chang, K. C. C. (2018). A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE transactions on knowledge and data engineering, 30(9), 1616-1637. [paper]
• Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., ... & Sun, M. (2020). Graph neural networks: A review of methods and applications. AI open, 1, 57-81. [paper]
• Gao, C., Zheng, Y., Li, N., Li, Y., Qin, Y., Piao, J., ... & Li, Y. (2021). Graph neural networks for recommender systems: Challenges, methods, and directions. arXiv preprint arXiv:2109.12843, 1, 46-58. [paper]
• Rampášek, L., Galkin, M., Dwivedi, V. P., Luu, A. T., Wolf, G., & Beaini, D. (2022). Recipe for a general, powerful, scalable graph transformer. Advances in Neural Information Processing Systems, 35, 14501-14515. [paper]
• Veličković, P. (2023). Everything is connected: Graph neural networks. Current Opinion in Structural Biology, 79, 102538. [paper]
• Wang, X., Bo, D., Shi, C., Fan, S., Ye, Y., & Philip, S. Y. (2022). A survey on heterogeneous graph embedding: methods, techniques, applications and sources. IEEE Transactions on Big Data, 9(2), 415-436. [paper]
• Fortunato, S. (2010). Community detection in graphs. Physics reports, 486(3-5), 75-174. [paper]
• Peng, B., Zhu, Y., Liu, Y., Bo, X., Shi, H., Hong, C., ... & Tang, S. (2024). Graph retrieval-augmented generation: A survey. arXiv preprint arXiv:2408.08921. [paper]

2. Aggregate architecture

• Perozzi, B., Al-Rfou, R., & Skiena, S. (2014, August). Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 701-710). [paper] :: [ paper_review ]

• Grover, A., & Leskovec, J. (2016, August). node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 855-864). [paper] :: [ paper_review ]

• Wang, D., Cui, P., & Zhu, W. (2016, August). Structural deep network embedding. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1225-1234). [paper] :: [ paper review ]

• Bo, D., Wang, X., Shi, C., Zhu, M., Lu, E., & Cui, P. (2020, April). Structural deep clustering network. In Proceedings of the web conference 2020 (pp. 1400-1410). [paper]

• Yanchenko, E., Murata, T., & Holme, P. (2023). Link prediction for ex ante influence maximization on temporal networks. Applied Network Science, 8(1), 70. [paper]

• Fu, X., Zhang, J., Meng, Z., & King, I. (2020, April). Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding. In Proceedings of the web conference 2020 (pp. 2331-2341). [paper]

• Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907. [paper] :: [ paper_review ]

• Li, R., Wang, S., Zhu, F., & Huang, J. (2018, April). Adaptive graph convolutional neural networks. In Proceedings of the AAAI conference on artificial intelligence (Vol. 32, No. 1). [pages]

• Li, G., Muller, M., Thabet, A., & Ghanem, B. (2019). Deepgcns: Can gcns go as deep as cnns?. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 9267-9276). [paper]

• Chiang, W. L., Liu, X., Si, S., Li, Y., Bengio, S., & Hsieh, C. J. (2019, July). Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 257-266). [paper]

• Pei, H., Wei, B., Chang, K. C. C., Lei, Y., & Yang, B. (2020). Geom-gcn: Geometric graph convolutional networks. arXiv preprint arXiv:2002.05287. [paper]

• Van Den Berg, R., Thomas, N. K., & Welling, M. (2017). Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263, 2(8), 9. [paper]

• Qiu, J., Chen, Q., Dong, Y., Zhang, J., Yang, H., Ding, M., ... & Tang, J. (2020, August). Gcc: Graph contrastive coding for graph neural network pre-training. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 1150-1160). [paper]

• Gao, H., Wang, Z., & Ji, S. (2018, July). Large-scale learnable graph convolutional networks. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 1416-1424). [paper]

• Wang, X., Zhu, M., Bo, D., Cui, P., Shi, C., & Pei, J. (2020, August). Am-gcn: Adaptive multi-channel graph convolutional networks. In Proceedings of the 26th ACM SIGKDD International conference on knowledge discovery & data mining (pp. 1243-1253). [paper]

• Gated Graph Sequence Neural Networks - GGNN

• Inductive Representation Learning on Large Graphs - GraphSAGE :: [ paper_review ]

• Composition-based multi-relational graph convolutional networks - CompGCN

• Modeling Relational Data with Graph Convolutional Networks - RGCN

• Graph Attention Networks - GAT :: [ paper_review ]

• Heterogeneous Graph Neural Network - HetGNN :: [ paper_review ]

• Heterogeneous graph Attention Network - HAN :: [ paper_review ]

• Hierarchical Graph Pooling with Structure Learning - HGP-SL

• Graph Transformer Networks - GTN

• Heterogeneous Graph Transformer

• Graph Inductive Biases in Transformers without Message Passing

• Do Transformer Really Perform Bad for Graph Representation - Graphormer :: [ paper_review ]

• Watch Your Step: Learning Node Embeddings via Graph Attention

• Universal Graph Transformer Self-Attention Networks

• metapath2vec: Scalable Representation Learning for Heterogeneous Networks :: [ paper_review ]

• Large Graph Property Prediction via Graph Segment Training

• TpuGraphs : A Performance Prediction Dataset on Large Tensor Computational Graphs

• Graph-bert: Only attention is needed for learning graph representations

3. Graph Pooling

• Graph U-Nets
• Hierarchical Graph Representation Learning with Differentiable Pooling
• Graph Convolutional Networks with EigenPooling
• Self-Attention Graph Pooling
• Hierarchical Graph Capsule Network
• Understanding Attention and Generalization in Graph Neural Networks

4. Temporal Graph Learning

• Graph Deep Learning for Time Series Forecasting :: [ Slides ]
• Using Causality-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs

5. Community Detection

• Graph Convolutional Networks Meet Markov Random Fields: Semi-Supervised Community Detection in Attribute Networks

6.Knowledge Graph

• Translating Embeddings for Modeling Multi-relational Data
• Learning Entity and Relation Embeddings for Knowledge Graph Completion
• Temporal Dynamics-Aware Adversarial Attacks on Discrete-Time Dynamic Graph Models
• Unifying Large Language Models and Knowledge Graphs: A Roadmap

7. Application

• Agarwal, M., Sun, M., Kamath, C., Muslim, A., Sarker, P., Paul, J., ... & Prasad, G. (2024). General Geospatial Inference with a Population Dynamics Foundation Model. arXiv preprint arXiv:2411.07207.
• Graph Convoultional Networks for Text Classification
• Graph-based Neural Multi-Document Summarization
• GNNExplainer : Generating Explanations for Graph Neural Networks :: [ paper_review ]
• GCN-LRP explanation: exploring latent attention of graph convolutional networks
• Graph Convolutional Neural Networks for Web-Scale Recommender Systems - PinSAGE
• Graph Neural Networks for Social Recommendation
• Knowledge Graph Self-Supervised Rationalization for Recommendation
• Graph Convolutional Networks for Hyperspectral Image Classification - miniGCN :: [ paper_review ]
• Deep Hierarchical Graph Convolution for Election Prediction from Geospatial Census Data
• Spatial context-aware method for urban land use classification using street view images :: [ paper_review ]
• A Heterogeneous Graph-based Framework for Scalable Fraud Detection
• Heterogeneous Graph Contrastive Learning for Recommendation
• Incorporating Graph Attention and Recurrent Architectures for City-Wide Taxi Demand Prediction
• TimeGraphs: Graph-based Temporal Reasoning
• Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection
• Graph Neural Architecture Search