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Graph representation learning [PDF]
, 2022 Graph is a type of structured data which is attracting increasing attention in recent years due to its strong capability in describing multiple objects as well as their relationships.
Peng Cui +4 more
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Hyperbolic Graph Representation Learning: A Tutorial [PDF]
CoRR, 2022 Accepted as ECML-PKDD 2022 ...
Min Zhou 0006 +3 more
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Disentangled Generative Graph Representation Learning
IEEE Transactions on Neural Networks and Learning SystemsRecently, generative graph models have shown promising results in learning graph representations through self-supervised methods. However, most existing generative graph representation learning (GRL) approaches rely on random masking across the entire graph, which overlooks the entanglement of learned representations.
Xinyue Hu +9 more
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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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Text-Graph Enhanced Knowledge Graph Representation Learning
Frontiers in Artificial Intelligence, 2021 Knowledge Graphs (KGs) such as Freebase and YAGO have been widely adopted in a variety of NLP tasks. Representation learning of Knowledge Graphs (KGs) aims to map entities and relationships into a continuous low-dimensional vector space.
Linmei Hu +6 more
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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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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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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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