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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
Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement LearningCode2
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource AllocationCode2
Random Walk on Multiple NetworksCode1
Learning Semantic Relationship Among Instances for Image-Text MatchingCode1
ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement LearningCode1
Multiplex Heterogeneous Graph Convolutional NetworkCode1
Online Knowledge Distillation via Mutual Contrastive Learning for Visual RecognitionCode1
Unsupervised Network Embedding Beyond HomophilyCode1
Monkey Business: Reinforcement learning meets neighborhood search for Virtual Network EmbeddingCode1
Signed Bipartite Graph Neural NetworksCode1
SiReN: Sign-Aware Recommendation Using Graph Neural NetworksCode1
A Survey on Role-Oriented Network EmbeddingCode1
Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic SpaceCode1
ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph NetworksCode1
Robust Dynamic Network Embedding via EnsemblesCode1
An Influence-based Approach for Root Cause Alarm Discovery in Telecom NetworksCode1
Mutual Contrastive Learning for Visual Representation LearningCode1
Fast Graph Learning with Unique Optimal SolutionsCode1
Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional NetworksCode1
SDGNN: Learning Node Representation for Signed Directed NetworksCode1
Introducing various Semantic Models for Amharic: Experimentation and Evaluation with multiple Tasks and DatasetsCode1
Sub-graph Contrast for Scalable Self-Supervised Graph Representation LearningCode1
Adversarial Privacy Preserving Graph Embedding against Inference AttackCode1
MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approachCode1
GloDyNE: Global Topology Preserving Dynamic Network EmbeddingCode1
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