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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 271280 of 403 papers

TitleStatusHype
Domain-adversarial Network AlignmentCode0
Deep Hashing for Signed Social Network Embedding0
ProNE: Fast and Scalable Network Representation LearningCode0
DynWalks: Global Topology and Recent Changes Awareness Dynamic Network EmbeddingCode1
IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for RecommendationCode0
Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling0
PathRank: A Multi-Task Learning Framework to Rank Paths in Spatial Networks0
Graph Representation Learning via Hard and Channel-Wise Attention NetworksCode0
Network Embedding: on Compression and LearningCode0
Signed Graph Attention NetworksCode1
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