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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

Showing 2130 of 403 papers

TitleStatusHype
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
Network Embedding with Completely-imbalanced LabelsCode1
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network EmbeddingCode1
Unsupervised Differentiable Multi-aspect Network EmbeddingCode1
Network Together: Node Classification via Cross-Network Deep Network EmbeddingCode1
Multi-View Collaborative Network EmbeddingCode1
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