Results 31 to 40 of about 8,038,825 (297)

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).
Zhe Chen 0017, Hao Tan, Tao Wang 0052, Tianrun Shen, Tong Lu 0002, Qiuying Peng, Cheng Cheng, Yue Qi   +7 more
openaire   +3 more sources

Learning Graph Representations With Maximal Cliques

IEEE Transactions on Neural Networks and Learning Systems, 2023
Non-Euclidean property of graph structures has faced interesting challenges when deep learning methods are applied. Graph convolutional networks (GCNs) can be regarded as one of the successful approaches to classification tasks on graph data, although the structure of this approach limits its performance.
Molaei, S, Bousejin, NG, Zare, H, Jalili, M, Pan, S   +4 more
openaire   +4 more sources

ATPGNN: Reconstruction of Neighborhood in Graph Neural Networks With Attention-Based Topological Patterns

IEEE Access, 2021
Graph Neural Networks (GNNs) have been applied in many fields of semi-supervised node classification for non-Euclidean data. However, some GNNs cannot make good use of positive information brought by nodes which are far away from each central node for ...
Kehao Wang, Hantao Qian, Xuming Zeng, Mozi Chen, Kezhong Liu, Kai Zheng, Pan Zhou, Dapeng Wu   +7 more
doaj   +1 more source

Graph Representation Learning Based on Multi-Channel Graph Convolutional Autoencoders [PDF]

Jisuanji gongcheng, 2023
This study proposes a graph representation learning model based on multi-channel graph convolutional autoencoders to address the limited ability of graph convolutional autoencoders in fusing node attributes and graph topology, and their inability to ...
YUAN Lining, HU Hao, LIU Zhao
doaj   +1 more source

Graph Representation Learning Method Based on Neural Ranking with Embedded Hyperbolic Layer [PDF]

Jisuanji gongcheng, 2020
To address the high complexity of existing graph representation learning methods,this paper proposes a new graph representation learning method to improve the learning efficiency while maintaining the representation performance of graph features.The ...
TANG Suqin, LIU Xiaomei, YUAN Lei
doaj   +1 more source

Robust Graph Representation Learning for Local Corruption Recovery

, 2023
The performance of graph representation learning is affected by the quality of graph input. While existing research usually pursues a globally smoothed graph embedding, we believe the rarely observed anomalies are as well harmful to an accurate ...
Zhou, B, Jiang, Y, Liang, J, Wang, Y, Gao, J, Zhang, X, Pan, S   +6 more
core   +1 more source

Graph Oversampling Method Based on Graph Latent Representation Distribution Learning [PDF]

Jisuanji kexue yu tansuo
In the real world, many graph datasets suffer from class imbalance issues, typically manifesting at the node, edge, and graph levels. Common oversampling-based methods for addressing graph-level imbalance often lead to model overfitting due to a lack of ...
REN Bo, DONG Minggang, YU Yang, LU Xianrui
doaj   +1 more source

Graph Representation Ensemble Learning

CoRR, 2019
Representation learning on graphs has been gaining attention due to its wide applicability in predicting missing links, and classifying and recommending nodes. Most embedding methods aim to preserve certain properties of the original graph in the low dimensional space.
Palash Goyal, Di Huang, Sujit Rokka Chhetri, Arquimedes Canedo, Jaya Shree, Evan Patterson   +5 more
openaire   +2 more sources

GAN‐based deep neural networks for graph representation learning

Engineering Reports, 2022
Graph representation learning has attracted increasing attention in a variety of applications that involve learning on non‐Euclidean data. Recently, generative adversarial networks(GAN) have been increasingly applied to the field of graph representation ...
Ming Zhao, Yinglong Zhang
doaj   +1 more source

Learning Graph Representations by Dendrograms

CoRR, 2018
Hierarchical graph clustering is a common technique to reveal the multi-scale structure of complex networks. We propose a novel metric for assessing the quality of a hierarchical clustering. This metric reflects the ability to reconstruct the graph from the dendrogram, which encodes the hierarchy. The optimal representation of the graph defines a class
Thomas Bonald, Bertrand Charpentier
openaire   +2 more sources