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目录概符号说明DualGCN代码 Wang Q., Wei Y., Yin J., Wu J., Song X. and Nie L. DualGNN: Dual graph neural network for multimedia recommendation. IEEE Transaction 阅读全文
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目录概符号说明MGCNMotivationBehavior-Guided PurifierMulti-View Information EncoderBehavior-Aware FuserPredicitonOptimation代码 Yu P., Tan Z., Lu G. and Bao B. 阅读全文
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目录概FREEDOMMotivationFrozen Item-Item graphDenoising User-Item Bipartite GraphTwo Graphs for Learning代码 Zhou X. and Shen Z. A tale of two graphs: Freez 阅读全文
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目录概符号说明LATTICEModality-aware Latent Structure LearningCombining with Collaborative Filtering代码 Zhang J., Zhu Y., Liu Q., Wu S., Wang S. and wang L. Mi 阅读全文
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目录概符号说明KGATEmbedding LayerAttentive Embedding Propagation Layers代码 Wang X., He X., Cao Y., Liu M. and Chua T. KGAT: Knowledge graph attention network 阅读全文
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目录概TransE|H|R Bordes A., Usunier N., Garcia-Duran A., Weston J. and Yakhnenko O. Translating embeddings for modeling multi-relational data. NIPS, 2013 阅读全文
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目录概符号说明ULTRA (a method for Unified, Learnable, and TRAnsferable KG representations)Relation Graph ConstructionConditional Relation Representations代码 G 阅读全文
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目录概符号说明MotivationNBFNet代码 Zhu Z., Zhang Z., Xhonneux L. and Tang J. Neural Bellman-Ford networks: A general graph neural network framework for link pr 阅读全文
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目录概符号说明PA-GNN Yang Y., Liang Y. and Zhang M. PA-GNN: Parameter-adaptive graph neural networks. ICML workshop, 2022. 概 一个自适应学习 GNN layer weights 的方法. 符 阅读全文
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目录概符号说明GPR-GNN代码 Chien E., Peng J., Li P. and Milenkovic O. Adaptive universal generalized pagerank graph neural network. ICLR, 2021. 概 GPR-GNN 自适应地学习 阅读全文
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目录概符号说明Popular homophily measures理想的准则现有的 metrics 的分析 Platonov O., Kuznedelev D., Babenko A. and Prokhorenkova L. Characterizing graph datasets for no 阅读全文
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目录概符号说明Homophily metricsPost-aggregation node similarity matrix代码 Luan S., Hua C., Lu Q., Zhu J., Zhao M., Zhang S., Chang X. and Precup D. Revisiting 阅读全文
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目录概符号说明Empirical AnalysisSkeleton GraphNode FetchingGraph Condensation代码 Cao L., Deng H., Wang C., Chen L. and Yang Y. Graph-skeleton: ~1% nodes are s 阅读全文
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目录概符号说明MotivationROLAND训练策略Live-update Evaluation代码 You J., Du T. and Leskovec J. ROLAND: Graph learning framework for dynamic graphs. KDD, 2022. 概 dy 阅读全文
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目录概符号说明EvolveGCN代码 Pareja A., Domeniconi G., Chen J., Ma T., Suzumura T., Kanezashi H., Kaler T., Schardl T. B. and Leiserson C. E. EvolveGCN: Evolvin 阅读全文
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目录概符号说明MotivationLTGNN代码 Zhang J., Xue R., Fan W., Xu X., Li Q., Pei J. and Liu X. Linear-time graph neural networks for scalable recommendations. WWW 阅读全文
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目录概符号说明MMGCN代码 Wei Y., Wang X., Nie L., He X., Hong R. and Chua T. MMGCN: Multi-modal graph convolution network for personalized recommendation of mic 阅读全文
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目录概符号说明GenAgg代码 Kortvelesy R., Morad S. and Prorok A. Generalised f-mean aggregation for graph neural networks. NIPS, 2023. 概 基于 MPNN 架构的 GNN 主要在于 agg 阅读全文
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目录概符号说明Cold Brew代码 Zheng W., Huang E. W., Rao N., Katariya S., Wang Z., Subbian K. Cold brew: Distilling graph node representations with incomplete or 阅读全文
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目录概符号说明MotivationGCOND代码 Jin W., Zhao L., Zhang S., Liu Y., Tang J. and Shah N. Graph condensation for graph neural networks. ICLR, 2022. 概 图上做压缩的工作. 阅读全文