Results 1 to 10 of about 3,653 (212)
Deep Attributed Network Embedding via Weisfeiler-Lehman and Autoencoder
IEEE Access, 2022 Network embedding plays a critical role in many applications. Node classification, link prediction, and network visualization are examples of such applications.
Amr Thabit Al-Furas +3 more
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Bi-pattern mining of attributed networks [PDF]
Applied Network Science, 2019 Applying closed pattern mining to attributed two-mode networks requires two conditions. First, as in two-mode networks there are two kinds of vertices, each described with a proper attribute set, we have to consider patterns made of two components that ...
Henry Soldano +4 more
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Enhancing Attributed Network Embedding via Similarity Measure
IEEE Access, 2019 Network embedding aims to represent network structural and attributed information with low-dimensional vectors, which has been demonstrated to be beneficial for many network analysis tasks, such as link prediction, node classification and visualization ...
Bin Yu +4 more
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Deep attributed network representation learning via attribute enhanced neighborhood
Neurocomputing, 2022 Attributed network representation learning aims at learning node embeddings by integrating network structure and attribute information. It is a challenge to fully capture the microscopic structure and the attribute semantics simultaneously, where the microscopic structure includes the one-step, two-step and multi-step relations, indicating the first ...
Cong Li 0009 +3 more
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Ricci Curvature-Based Semi-Supervised Learning on an Attributed Network
Entropy, 2021 In recent years, on the basis of drawing lessons from traditional neural network models, people have been paying more and more attention to the design of neural network architectures for processing graph structure data, which are called graph neural ...
Wei Wu, Guangmin Hu, Fucai Yu
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Recommendation algorithm based on attributed multiplex heterogeneous network [PDF]
PeerJ Computer Science, 2021 In the field of deep learning, the processing of large network models on billions or even tens of billions of nodes and numerous edge types is still flawed, and the accuracy of recommendations is greatly compromised when large network embeddings are ...
Zhisheng Yang, Jinyong Cheng
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Hierarchical Labels Guided Attributed Network Embedding
Jisuanji kexue yu tansuo, 2021 Network embedding, aiming to learn low dimensional vectors for nodes while preserving important properties of the network, benefits plenty of network applications.
CHEN Jie, CHEN Jialin, ZHAO Shu, ZHANG Yanping
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Effective attributed network embedding with information behavior extraction [PDF]
PeerJ Computer Science, 2022 Network embedding has shown its effectiveness in many tasks, such as link prediction, node classification, and community detection. Most attributed network embedding methods consider topological features and attribute features to obtain a node embedding ...
Ganglin Hu, Jun Pang, Xian Mo
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Attributed Graph Embedding with Random Walk Regularization and Centrality-Based Attention
Mathematics, 2023 Graph-embedding learning is the foundation of complex information network analysis, aiming to represent nodes in a graph network as low-dimensional dense real-valued vectors for the application in practical analysis tasks.
Yuxuan Yang +4 more
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Attributed Bipartite Network Representation Learning
Jisuanji kexue yu tansuo, 2021 Existing network embedding models are mostly designed for homogeneous networks or heterogeneous networks, but ignore the special features of bipartite network which arise in recommender systems, search engines, question answering systems and so on ...
ZHAO Xueli, LU Guangyue, LV Shaoqing, ZHANG Pan
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