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Network Embedding

Network Embedding, also known as "Network Representation Learning", is a collective term for techniques for mapping graph nodes to vectors of real numbers in a multidimensional space. To be useful, a good embedding should preserve the structure of the graph. The vectors can then be used as input to various network and graph analysis tasks, such as link prediction

Source: Tutorial on NLP-Inspired Network Embedding

Papers

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Showing 126–150 of 403 papers

TitleStatusHype
Efficient Network Embedding by Approximate Equitable PartitionsCode0
Learning Role-based Graph EmbeddingsCode0
Improving Textual Network Learning with Variational Homophilic EmbeddingsCode0
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask DependenciesCode0
A novel robust integrating method by high-order proximity for self-supervised attribute network embeddingCode0
HAHE: Hierarchical Attentive Heterogeneous Information Network EmbeddingCode0
Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization PerspectiveCode0
Graph Representation Learning via Hard and Channel-Wise Attention NetworksCode0
Integrating Network Embedding and Community Outlier Detection via Multiclass Graph DescriptionCode0
Enhanced Network Embedding with Text InformationCode0
Global Vectors for Node RepresentationsCode0
GraphVite: A High-Performance CPU-GPU Hybrid System for Node EmbeddingCode0
H^2TNE: Temporal Heterogeneous Information Network Embedding in Hyperbolic SpacesCode0
GENE: Global Event Network EmbeddingCode0
A Non-negative Symmetric Encoder-Decoder Approach for Community DetectionCode0
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data ManifoldsCode0
Exploiting Node Content for Multiview Graph Convolutional Network and Adversarial RegularizationCode0
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network EmbeddingCode0
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information NetworksCode0
CANE: Context-Aware Network Embedding for Relation ModelingCode0
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money LaunderingCode0
Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node DescriptorsCode0
Font Size: Community Preserving Network EmbeddingCode0
Flexible Attributed Network EmbeddingCode0
Dynamic Embedding on Textual Networks via a Gaussian ProcessCode0
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