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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 281–290 of 403 papers

TitleStatusHype
IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for RecommendationCode0
Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling—0
PathRank: A Multi-Task Learning Framework to Rank Paths in Spatial Networks—0
Graph Representation Learning via Hard and Channel-Wise Attention NetworksCode0
Network Embedding: on Compression and LearningCode0
NetSMF: Large-Scale Network Embedding as Sparse Matrix FactorizationCode0
Dynamic Network Embeddings for Network Evolution Analysis—0
ANAE: Learning Node Context Representation for Attributed Network Embedding—0
DISCO: Influence Maximization Meets Network Embedding and Deep Learning—0
Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank—0
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