Results 11 to 20 of about 78,912 (260)
Asymmetric Graph Representation Learning
CoRR, 2021 Despite the enormous success of graph neural networks (GNNs), most existing GNNs can only be applicable to undirected graphs where relationships among connected nodes are two-way symmetric (i.e., information can be passed back and forth). However, there is a vast amount of applications where the information flow is asymmetric, leading to directed ...
Zhuo Tan, Bin Liu 0022, Guosheng Yin
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Improved Skip-Gram Based on Graph Structure Information
Sensors, 2023 Applying the Skip-gram to graph representation learning has become a widely researched topic in recent years. Prior works usually focus on the migration application of the Skip-gram model, while Skip-gram in graph representation learning, initially ...
Xiaojie Wang, Haijun Zhao, Huayue Chen
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Clustering Method Based on Contrastive Learning for Multi-relation Attribute Graph [PDF]
Jisuanji kexue, 2023 In the real world,there are many complex graph data which includes multiple relations between nodes,namely multi-relation attribute graph.Graph clustering is one of the approaches for mining similar information from graph data.However,most existing graph
XIE Zhuo, KANG Le, ZHOU Lijuan, ZHANG Zhihong
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Graph Representation Ensemble Learning [PDF]
2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2020 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 specific properties of the original graph in the low dimensional space. However, real-world graphs have a combination of several features that are difficult
Palash Goyal +7 more
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Learning Graph Augmentations to Learn Graph Representations
CoRR, 2022 Devising augmentations for graph contrastive learning is challenging due to their irregular structure, drastic distribution shifts, and nonequivalent feature spaces across datasets. We introduce LG2AR, Learning Graph Augmentations to Learn Graph Representations, which is an end-to-end automatic graph augmentation framework that helps encoders learn ...
Hassani, Kaveh, Khasahmadi, Amir Hosein
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Advances in the Development of Representation Learning and Its Innovations against COVID-19
COVID, 2023 In bioinformatics research, traditional machine-learning methods have demonstrated efficacy in addressing Euclidean data. However, real-world data often encompass non-Euclidean forms, such as graph data, which contain intricate structural patterns or ...
Peng Li +4 more
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A survey of information network representation learning
Journal of Hebei University of Science and Technology, 2020 The network representation learning algorithm represents the information network as a low-dimensional dense real vector carrying the characteristic information of network nodes, and is applied to the input of downstream machine learning tasks.
Junhao LU, Yunfeng XU
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Adaptive Graph Representation for Clustering
IEEE Access, 2022 Many graph construction methods for clustering cannot consider both local and global data structures in the construction of initial graph. Meanwhile, redundant features or even outliers and data with important characteristics are addressed equally in the
Mei Chen +5 more
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Motif-Aware Adversarial Graph Representation Learning
IEEE Access, 2022 Graph representation learning has been extensively studied in recent years. It has been proven effective in network analysis and mining tasks such as node classification and link prediction.
Ming Zhao +3 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).
Zhe Chen 0017 +7 more
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