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Deep Inductive Graph Representation Learning [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2020 This paper presents a general inductive graph representation learning framework called $\text{DeepGL}$ DeepGL for learning deep node and edge features that generalize across-networks. In particular, $\text{DeepGL}$ DeepGL begins by deriving a set of base features from the graph (e.g., graphlet features) and automatically learns a ...
Ryan A. Rossi +2 more
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Graph representation learning: a survey [PDF]
APSIPA Transactions on Signal and Information Processing, 2020 Research on graph representation learning has received a lot of attention in recent years since many data in real-world applications come in form of graphs. High-dimensional graph data are often in irregular form, which makes them more difficult to analyze than image/video/audio data defined on regular lattices.
Chen, Fenxiao +3 more
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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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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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Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion [PDF]
, 2021 Human-curated knowledge graphs provide critical supportive information to various natural language processing tasks, but these graphs are usually incomplete, urging auto-completion of them.
Bo Wang +5 more
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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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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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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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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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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 +4 more
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