Results 31 to 40 of about 537,310 (265)

Local structure-aware graph contrastive representation learning

Neural Networks, 2023
Traditional Graph Neural Network (GNN), as a graph representation learning method, is constrained by label information. However, Graph Contrastive Learning (GCL) methods, which tackle the label problem effectively, mainly focus on the feature information of the global graph or small subgraph structure (e.g., the first-order neighborhood). In the paper,
Yang, Kai, Liu, Yuan, Zhao, Zijuan, Ding, Peijin, Zhao, Wenqian   +4 more
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A review on graph-based semi-supervised learning methods for hyperspectral image classification

Egyptian Journal of Remote Sensing and Space Sciences, 2020
In this article, a comprehensive review of the state-of-art graph-based learning methods for classification of the hyperspectral images (HSI) is provided, including a spectral information based graph semi-supervised classification and a spectral-spatial ...
Shrutika S. Sawant, Manoharan Prabukumar
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Scientific Paper Heterogeneous Graph Node Representation Learning Method Based onUnsupervised Clustering Level [PDF]

Jisuanji kexue, 2022
Knowledge representation of scientific paper data is a problem to be solved,and how to learn the representation of paper nodes in scientific paper heterogeneous network is the core to solve this problem.This paper proposes an unsupervised cluster-level ...
SONG Jie, LIANG Mei-yu, XUE Zhe, DU Jun-ping, KOU Fei-fei
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FunQG: Molecular Representation Learning via Quotient Graphs

Journal of Chemical Information and Modeling, 2023
Learning expressive molecular representations is crucial to facilitate the accurate prediction of molecular properties. Despite the significant advancement of graph neural networks (GNNs) in molecular representation learning, they generally face limitations such as neighbors-explosion, under-reaching, over-smoothing, and over-squashing.
Hossein Hajiabolhassan, Zahra Taheri, Ali Hojatnia, Yavar Taheri Yeganeh   +3 more
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Temporal network embedding framework with causal anonymous walks representations [PDF]

PeerJ Computer Science, 2022
Many tasks in graph machine learning, such as link prediction and node classification, are typically solved using representation learning. Each node or edge in the network is encoded via an embedding.
Ilya Makarov, Andrey Savchenko, Arseny Korovko, Leonid Sherstyuk, Nikita Severin, Dmitrii Kiselev, Aleksandr Mikheev, Dmitrii Babaev   +7 more
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CoLM2S: Contrastive self‐supervised learning on attributed multiplex graph network with multi‐scale information

CAAI Transactions on Intelligence Technology, 2023
Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently. However, there are still two challenges.
Beibei Han, Yingmei Wei, Qingyong Wang, Shanshan Wan   +3 more
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Assessment instrument of graph representations on sound wave topic: Development and measurement implementation

Knowledge Management & E-Learning: An International Journal
Sound waves are one of the important topics studied in physics. However, students’ graph representation is still low, leading to their low concept understanding of physics learning.
Pramudya Wahyu Pradana, Supahar
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Graph-Based Text Representation and Matching: A Review of the State of the Art and Future Challenges

IEEE Access, 2020
Graph-based text representation is one of the important preprocessing steps in data and text mining, Natural Language Processing (NLP), and information retrieval approaches. The graph-based methods focus on how to represent text documents in the shape of
Ahmed Hamza Osman, Omar Mohammed Barukub
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Multi-View Spectral Clustering Based on Multi-Smooth Representation Fusion for Cancer Subtype Prediction

Frontiers in Genetics, 2021
It is a vital task to design an integrated machine learning model to discover cancer subtypes and understand the heterogeneity of cancer based on multiple omics data.
Jian Liu, Jian Liu, Shuguang Ge, Shuguang Ge, Yuhu Cheng, Yuhu Cheng, Xuesong Wang, Xuesong Wang   +7 more
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Graph Propagation Transformer for Graph Representation Learning

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023
This paper presents a novel transformer architecture for graph representation learning. The core insight of our method is to fully consider the information propagation among nodes and edges in a graph when building the attention module in the transformer blocks. Specifically, we propose a new attention mechanism called Graph Propagation Attention (GPA).
Chen, Zhe, Tan, Hao, Wang, Tao, Shen, Tianrun, Lu, Tong, Peng, Qiuying, Cheng, Cheng, Qi, Yue   +7 more
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