Graph neural networks

被引：0
|
作者：
Corso G. [1 ]
Stark H. [1 ]
Jegelka S. [1 ,2 ]
Jaakkola T. [1 ]
Barzilay R. [1 ]
机构：
[1] CSAIL, MIT, Cambridge, MA
[2] School of CIT, TU Munich, Munich
来源：
Nature Reviews Methods Primers | / 4卷 / 1期
基金：
美国国家科学基金会;
关键词：
D O I：
10.1038/s43586-024-00294-7
中图分类号：
学科分类号：
摘要：
iGraphs are flexible mathematical objects that can represent many entities and knowledge from different domains, including in the life sciences. Graph neural networks (GNNs) are mathematical models that can learn functions over graphs and are a leading approach for building predictive models on graph-structured data. This combination has enabled GNNs to advance the state of the art in many disciplines, from discovering new antibiotics and identifying drug-repurposing candidates to modelling physical systems and generating new molecules. This Primer provides a practical and accessible introduction to GNNs, describing their properties and applications to the life and physical sciences. Emphasis is placed on the practical implications of key theoretical limitations, new ideas to solve these challenges and important considerations when using GNNs on a new task. © Springer Nature Limited 2024.
引用
收藏
相关论文
共 50 条
  • [1] Graph neural networks
    不详
    [J]. NATURE REVIEWS METHODS PRIMERS, 2024, 4 (01):
  • [2] Graph Neural Networks for Graph Drawing
    Tiezzi, Matteo
    Ciravegna, Gabriele
    Gori, Marco
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2024, 35 (04) : 4668 - 4681
  • [3] Graph Mining with Graph Neural Networks
    Jin, Wei
    [J]. WSDM '21: PROCEEDINGS OF THE 14TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, 2021, : 1119 - 1120
  • [4] Graph Clustering with Graph Neural Networks
    Tsitsulin, Anton
    Palowitch, John
    Perozzi, Bryan
    Mueller, Emmanuel
    [J]. JOURNAL OF MACHINE LEARNING RESEARCH, 2023, 24
  • [5] Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks
    Gama, Fernando
    Isufi, Elvin
    Leus, Geert
    Ribeiro, Alejandro
    [J]. IEEE SIGNAL PROCESSING MAGAZINE, 2020, 37 (06) : 128 - 138
  • [6] Graphon Neural Networks and the Transferability of Graph Neural Networks
    Ruiz, Luana
    Chamon, Luiz F. O.
    Ribeiro, Alejandro
    [J]. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020, 2020, 33
  • [7] Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
    Du, Simon S.
    Hou, Kangcheng
    Poczos, Barnabas
    Salakhutdinov, Ruslan
    Wang, Ruosong
    Xu, Keyulu
    [J]. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), 2019, 32
  • [8] Rethinking Graph Regularization for Graph Neural Networks
    Yang, Han
    Ma, Kaili
    Cheng, James
    [J]. THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2021, 35 : 4573 - 4581
  • [9] Graph Neural Networks with Local Graph Parameters
    Barcelo, Pablo
    Geerts, Floris
    Reutter, Juan
    Ryschkov, Maksimilian
    [J]. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021), 2021, 34
  • [10] Semisupervised Graph Neural Networks for Graph Classification
    Xie, Yu
    Liang, Yanfeng
    Gong, Maoguo
    Qin, A. K.
    Ong, Yew-Soon
    He, Tiantian
    [J]. IEEE TRANSACTIONS ON CYBERNETICS, 2023, 53 (10) : 6222 - 6235
12345