圖神經網絡 (GNN) 是一種連接模型,它通過圖的節點之間的消息傳遞來捕捉圖的依賴關係。與標準神經網絡不同的是,圖神經網絡保留了一種狀態,可以表示來自其鄰域的具有任意深度的信息。近年來,圖神經網絡(GNN)在社交網絡、知識圖、推薦係統、問答係統甚至生命科學等各個領域得到了越來越廣泛的應用。

知識薈萃

圖神經網絡(Graph Neural Networks, GNN)專知薈萃

入門

綜述

  • 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

進階論文

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

應用

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

視頻教程

代碼

領域專家

VIP內容

這是由Ruslan Salakhutdinov教授在卡內基梅隆大學的深度學習課程上的一個客座講座。本演講介紹了1)圖神經網絡的一般概念2)該領域中有趣的開放研究問題。它針對的是具有深度學習背景但不了解(或很少)圖神經網絡知識的人。

成為VIP會員查看完整內容
3
17
0

最新內容

最新論文

Graph Neural Networks (GNNs) have garnered a lot of recent interest because of their success in learning representations from graph-structured data across several critical applications in cloud and HPC. Owing to their unique compute and memory characteristics that come from an interplay between dense and sparse phases of computations, the emergence of reconfigurable dataflow (aka spatial) accelerators offers promise for acceleration by mapping optimized dataflows (i.e., computation order and parallelism) for both phases. The goal of this work is to characterize and understand the design-space of dataflow choices for running GNNs on spatial accelerators in order for mappers or design-space exploration tools to optimize the dataflow based on the workload. Specifically, we propose a taxonomy to describe all possible choices for mapping the dense and sparse phases of GNN inference, spatially and temporally over a spatial accelerator, capturing both the intra-phase dataflow and the inter-phase (pipelined) dataflow. Using this taxonomy, we do deep-dives into the cost and benefits of several dataflows and perform case studies on implications of hardware parameters for dataflows and value of flexibility to support pipelined execution.

0
0
0
下載
預覽
參考鏈接
父主題
微信掃碼谘詢專知VIP會員
Top