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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 26–50 of 403 papers

TitleStatusHype
DynWalks: Global Topology and Recent Changes Awareness Dynamic Network EmbeddingCode1
SDGNN: Learning Node Representation for Signed Directed NetworksCode1
Signed Bipartite Graph Neural NetworksCode1
SiReN: Sign-Aware Recommendation Using Graph Neural NetworksCode1
An Influence-based Approach for Root Cause Alarm Discovery in Telecom NetworksCode1
Fast Network Embedding Enhancement via High Order Proximity ApproximationCode1
Network Embedding with Completely-imbalanced LabelsCode1
DANE: Domain Adaptive Network EmbeddingCode1
Fast Sequence Based Embedding with Diffusion GraphsCode1
Adversarial Training Methods for Network EmbeddingCode1
GloDyNE: Global Topology Preserving Dynamic Network EmbeddingCode1
HiGitClass: Keyword-Driven Hierarchical Classification of GitHub RepositoriesCode1
ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph NetworksCode1
Adaptive Graph Auto-Encoder for General Data ClusteringCode1
Learning Semantic Relationship Among Instances for Image-Text MatchingCode1
LouvainNE: Hierarchical Louvain Method for High Quality and Scalable Network Embedding.Code1
A Survey on Role-Oriented Network EmbeddingCode1
Representation Learning for Attributed Multiplex Heterogeneous NetworkCode1
Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNNCode1
Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional NetworksCode1
Multiplex Heterogeneous Graph Convolutional NetworkCode1
Fast and Accurate Network Embeddings via Very Sparse Random ProjectionCode1
Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic SpaceCode1
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network EmbeddingCode1
Unsupervised Differentiable Multi-aspect Network EmbeddingCode1
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