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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 151–175 of 403 papers

TitleStatusHype
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask DependenciesCode0
CANE: Context-Aware Network Embedding for Relation ModelingCode0
MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional NetworksCode0
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information NetworksCode0
Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node DescriptorsCode0
Is a Single Vector Enough? Exploring Node Polysemy for Network EmbeddingCode0
ASD Classification on Dynamic Brain Connectome using Temporal Random Walk with Transformer-based Dynamic Network EmbeddingCode0
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money LaunderingCode0
mvn2vec: Preservation and Collaboration in Multi-View Network EmbeddingCode0
GENE: Global Event Network EmbeddingCode0
Cross-Network Social User Embedding with Hybrid Differential Privacy GuaranteesCode0
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data ManifoldsCode0
A Simple and Powerful Framework for Stable Dynamic Network EmbeddingCode0
Global Vectors for Node RepresentationsCode0
RiWalk: Fast Structural Node Embedding via Role IdentificationCode0
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network EmbeddingCode0
Efficient Inner Product Approximation in Hybrid Spaces—0
Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks—0
Initialization for Network Embedding: A Graph Partition Approach—0
dynnode2vec: Scalable Dynamic Network Embedding—0
A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning—0
Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning—0
Big Networks: A Survey—0
Dynamic Network Embedding Survey—0
Dynamic Network Embeddings for Network Evolution Analysis—0
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