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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 276–300 of 403 papers

TitleStatusHype
Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling—0
PathRank: A Multi-Task Learning Framework to Rank Paths in Spatial Networks—0
Graph Representation Learning via Hard and Channel-Wise Attention NetworksCode0
Network Embedding: on Compression and LearningCode0
Signed Graph Attention NetworksCode1
NetSMF: Large-Scale Network Embedding as Sparse Matrix FactorizationCode0
Dynamic Network Embeddings for Network Evolution Analysis—0
ANAE: Learning Node Context Representation for Attributed Network Embedding—0
DISCO: Influence Maximization Meets Network Embedding and Deep Learning—0
Network Representation of Large-Scale Heterogeneous RNA Sequences with Integration of Diverse Multi-omics, Interactions, and Annotations Data—0
Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank—0
Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node DescriptorsCode0
Dynamic Network Embedding via Incremental Skip-gram with Negative SamplingCode0
Improving Textual Network Embedding with Global Attention via Optimal Transport—0
Task-Guided Pair Embedding in Heterogeneous NetworkCode0
DANE: Domain Adaptive Network EmbeddingCode1
Is a Single Vector Enough? Exploring Node Polysemy for Network EmbeddingCode0
Relation Structure-Aware Heterogeneous Information Network Embedding—0
ActiveHNE: Active Heterogeneous Network Embedding—0
Representation Learning for Attributed Multiplex Heterogeneous NetworkCode1
Physiological Signal Embeddings (PHASE) via Interpretable Stacked Models—0
ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions—0
Tag2Vec: Learning Tag Representations in Tag Networks—0
Compositional Network Embedding—0
Data driven approximation of parametrized PDEs by Reduced Basis and Neural NetworksCode0
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