图神经网络 (GNN) 是一种连接模型,它通过图的节点之间的消息传递来捕捉图的依赖关系。与标准神经网络不同的是,图神经网络保留了一种状态,可以表示来自其邻域的具有任意深度的信息。近年来,图神经网络(GNN)在社交网络、知识图、推荐系统、问答系统甚至生命科学等各个领域得到了越来越广泛的应用。

知识荟萃

图神经网络(Graph Neural Networks, GNN)专知荟萃

入门

综述

  • A Comprehensive Survey on Graph Neural Networks. Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu. 2019
    https://arxiv.org/pdf/190-00596.pdf
  • Relational inductive biases, deep learning, and graph networks. Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu. 2018.
    https://arxiv.org/pdf/1806.0126-pdf
  • Attention models in graphs. John Boaz Lee, Ryan A. Rossi, Sungchul Kim, Nesreen K. Ahmed, Eunyee Koh. 2018.
    https://arxiv.org/pdf/1807.07984.pdf
  • Deep learning on graphs: A survey. Ziwei Zhang, Peng Cui and Wenwu Zhu. 2018.
    https://arxiv.org/pdf/1812.04202.pdf
  • Graph Neural Networks: A Review of Methods and Applications. Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun. 2018
    https://arxiv.org/pdf/1812.08434.pdf
  • Geometric deep learning: going beyond euclidean data. Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, Pierre Vandergheynst. 2016.
    https://arxiv.org/pdf/161-08097.pdf

进阶论文

Recurrent Graph Neural Networks

Convolutional Graph Neural Networks

Spectral and Spatial

Architecture

Attention Mechanisms

Convolution

Training Methods

Pooling

Bayesian

Analysis

GAE

Spatial-Temporal Graph Neural Networks

应用

Physics

Knowledge Graph

Recommender Systems

  • STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems. Jiani Zhang, Xingjian Shi, Shenglin Zhao, Irwin King. IJCAI 2019.
    https://arxiv.org/pdf/1905.13129.pdf

  • Binarized Collaborative Filtering with Distilling Graph Convolutional Networks. Haoyu Wang, Defu Lian, Yong Ge. IJCAI 2019.
    https://arxiv.org/pdf/1906.01829.pdf

  • Graph Contextualized Self-Attention Network for Session-based Recommendation. Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, Xiaofang Zhou. IJCAI 2019.
    https://www.ijcai.org/proceedings/2019/0547.pdf

  • Session-based Recommendation with Graph Neural Networks. Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan. AAAI 2019.
    https://arxiv.org/pdf/181-00855.pdf

  • Geometric Hawkes Processes with Graph Convolutional Recurrent Neural Networks. Jin Shang, Mingxuan Sun. AAAI 2019.
    https://jshang2.github.io/pubs/geo.pdf

  • Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems. Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, Zhongyuan Wang. KDD 2019.
    https://arxiv.org/pdf/1905.04413

  • Exact-K Recommendation via Maximal Clique Optimization. Yu Gong, Yu Zhu, Lu Duan, Qingwen Liu, Ziyu Guan, Fei Sun, Wenwu Ou, Kenny Q. Zhu. KDD 2019.
    https://arxiv.org/pdf/1905.07089

  • KGAT: Knowledge Graph Attention Network for Recommendation. Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua. KDD 2019.
    https://arxiv.org/pdf/1905.07854

  • Knowledge Graph Convolutional Networks for Recommender Systems. Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, Minyi Guo. WWW 2019.
    https://arxiv.org/pdf/1904.12575.pdf

  • Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems. Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen. WWW 2019.
    https://arxiv.org/pdf/1903.10433.pdf

  • Graph Neural Networks for Social Recommendation. Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin. WWW 2019.
    https://arxiv.org/pdf/1902.07243.pdf

  • Graph Convolutional Neural Networks for Web-Scale Recommender Systems. Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec. KDD 2018.
    https://arxiv.org/abs/1806.01973

  • Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks. Federico Monti, Michael M. Bronstein, Xavier Bresson. NIPS 2017.
    https://arxiv.org/abs/1704.06803

  • Graph Convolutional Matrix Completion. Rianne van den Berg, Thomas N. Kipf, Max Welling. 2017.
    https://arxiv.org/abs/1706.02263

Computer Vision

Natural Language Processing

Others

Tutorial

视频教程

代码

领域专家

VIP内容

图神经网络(GNNs)被广泛用于学习一种强大的图结构数据表示。最近的研究表明,将知识从自监督任务迁移到下游任务可以进一步改善图的表示。然而,自监督任务与下游任务在优化目标和训练数据上存在内在的差距。传统的预训练方法可能对知识迁移不够有效,因为它们不能适应下游任务。为了解决这一问题,我们提出了一种新的迁移学习范式,该范式可以有效地将自监督任务作为辅助任务来帮助目标任务。在微调阶段,我们的方法将不同的辅助任务与目标任务进行自适应的选择和组合。我们设计了一个自适应辅助损失加权模型,通过量化辅助任务与目标任务之间的一致性来学习辅助任务的权重。此外,我们通过元学习来学习权重模型。我们的方法可以运用于各种迁移学习方法,它不仅在多任务学习中有很好的表现,而且在预训练和微调中也有很好的表现。在多个下游任务上的综合实验表明,所提出的方法能够有效地将辅助任务与目标任务相结合,与现有的方法相比,显著提高了性能。

https://www.zhuanzhi.ai/paper/852db932624d6feeb7bbd32e67772b27

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最新论文

Node features and structural information of a graph are both crucial for semi-supervised node classification problems. A variety of graph neural network (GNN) based approaches have been proposed to tackle these problems, which typically determine output labels through feature aggregation. This can be problematic, as it implies conditional independence of output nodes given hidden representations, despite their direct connections in the graph. To learn the direct influence among output nodes in a graph, we propose the Explicit Pairwise Factorized Graph Neural Network (EPFGNN), which models the whole graph as a partially observed Markov Random Field. It contains explicit pairwise factors to model output-output relations and uses a GNN backbone to model input-output relations. To balance model complexity and expressivity, the pairwise factors have a shared component and a separate scaling coefficient for each edge. We apply the EM algorithm to train our model, and utilize a star-shaped piecewise likelihood for the tractable surrogate objective. We conduct experiments on various datasets, which shows that our model can effectively improve the performance for semi-supervised node classification on graphs.

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