Survey of Breast Cancer Pathological Image Analysis Methods Based on Graph Neural Networks [PDF]
Jisuanji kexuePathological diagnosis is the gold standard for cancer diagnosis and treatment,the use of artificial intelligence(AI) models for analyzing pathological images has the potential to not only reduce the workload of pathologists but also improve the accuracy
CHEN Sishuo, WANG Xiaodong, LIU Xiyang
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A graph neural network with negative message passing and uniformity maximization for graph coloring
Complex & Intelligent SystemsGraph neural networks have received increased attention over the past years due to their promising ability to handle graph-structured data, which can be found in many real-world problems such as recommender systems and drug synthesis.
Xiangyu Wang, Xueming Yan, Yaochu Jin
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A review on the applications of graph neural networks in materials science at the atomic scale
Materials Genome Engineering AdvancesIn recent years, interdisciplinary research has become increasingly popular within the scientific community. The fields of materials science and chemistry have also gradually begun to apply the machine learning technology developed by scientists from ...
Xingyue Shi +4 more
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Framework and Algorithms for Accelerating Training of Semi-supervised Graph Neural Network Based on Heuristic Coarsening Algorithms [PDF]
Jisuanji kexueGraph neural network is the mainstream tool of graph machine learning at the current stage,and it has broad development prospects.By constructing an abstract graph structure,the graph neural network model can be used to efficiently deal with problems in ...
CHEN Yufeng , HUANG Zengfeng
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Hyperbolic Graph Convolutional Neural Networks
Advances in neural information processing systems, 2019 Published at Conference NeurIPS 2019.
Chami, Ines +3 more
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The Graph Neural Network Model
IEEE Transactions on Neural Networks, 2009 Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural ...
SCARSELLI, FRANCO +4 more
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Cascade2vec: Learning Dynamic Cascade Representation by Recurrent Graph Neural Networks
IEEE Access, 2019 An information dissemination network (i.e., a cascade) with a dynamic graph structure is formed when a novel idea or message spreads from person to person.
Zhenhua Huang, Zhenyu Wang, Rui Zhang
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IEEE AccessCryptocurrency money laundering is a pressing issue, as it not only facilitates and hides criminal activities but also disrupts markets and the overall financial system.
Stefano Ferretti +2 more
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Representing Long-Range Context for Graph Neural Networks with Global Attention [PDF]
, 2022 Zhanghao Wu +5 more
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A Unified Lottery Ticket Hypothesis for Graph Neural Networks [PDF]
, 2021 Tianlong Chen +4 more
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