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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 1–25 of 403 papers

TitleStatusHype
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource AllocationCode2
Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement LearningCode2
Learning Semantic Relationship Among Instances for Image-Text MatchingCode1
Fast Sequence-Based Embedding with Diffusion GraphsCode1
ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph NetworksCode1
Inductive Document Network Embedding with Topic-Word AttentionCode1
Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic SpaceCode1
Adversarial Privacy Preserving Graph Embedding against Inference AttackCode1
Fast Graph Learning with Unique Optimal SolutionsCode1
Fast Sequence Based Embedding with Diffusion GraphsCode1
HiGitClass: Keyword-Driven Hierarchical Classification of GitHub RepositoriesCode1
Adversarial Deep Network Embedding for Cross-network Node ClassificationCode1
An Influence-based Approach for Root Cause Alarm Discovery in Telecom NetworksCode1
Introducing various Semantic Models for Amharic: Experimentation and Evaluation with multiple Tasks and DatasetsCode1
DynWalks: Global Topology and Recent Changes Awareness Dynamic Network EmbeddingCode1
DANE: Domain Adaptive Network EmbeddingCode1
Adversarial Training Methods for Network EmbeddingCode1
A Survey on Role-Oriented Network EmbeddingCode1
Adaptive Graph Auto-Encoder for General Data ClusteringCode1
Fast and Accurate Network Embeddings via Very Sparse Random ProjectionCode1
Representation Learning for Attributed Multiplex Heterogeneous NetworkCode1
Fast Network Embedding Enhancement via High Order Proximity ApproximationCode1
GloDyNE: Global Topology Preserving Dynamic Network EmbeddingCode1
Heterogeneous Network Representation Learning: A Unified Framework with Survey and BenchmarkCode1
LINE: Large-scale Information Network EmbeddingCode1
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