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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 151–160 of 403 papers

TitleStatusHype
Adversarial Attack on Network Embeddings via Supervised Network PoisoningCode0
Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional NetworksCode1
Learning multi-resolution representations of research patterns in bibliographic networksCode0
NEMR: Network Embedding on Metric of Relation—0
Exact Recovery of Community Structures Using DeepWalk and Node2vec—0
JITuNE: Just-In-Time Hyperparameter Tuning for Network Embedding Algorithms—0
Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search—0
SDGNN: Learning Node Representation for Signed Directed NetworksCode1
Signed Graph Diffusion Network—0
GAHNE: Graph-Aggregated Heterogeneous Network Embedding—0
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