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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 151–175 of 403 papers

TitleStatusHype
Compositional Network Embedding—0
On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications—0
Full-Network Embedding in a Multimodal Embedding Pipeline—0
Exact Recovery of Community Structures Using DeepWalk and Node2vec—0
Fusion of Minutia Cylinder Codes and Minutia Patch Embeddings for Latent Fingerprint Recognition—0
GAHNE: Graph-Aggregated Heterogeneous Network Embedding—0
Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation—0
Controlled Deep Reinforcement Learning for Optimized Slice Placement—0
An Out-of-the-box Full-network Embedding for Convolutional Neural Networks—0
EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction—0
Genome Sequence Classification for Animal Diagnostics with Graph Representations and Deep Neural Networks—0
COSINE: Compressive Network Embedding on Large-scale Information Networks—0
Learning Features of Network Structures Using Graphlets—0
Cross Version Defect Prediction with Class Dependency Embeddings—0
Equivalence between LINE and Matrix Factorization—0
AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks—0
Associative Learning for Network Embedding—0
Grammar-Based Grounded Lexicon Learning—0
Unsupervised Graph Embedding via Adaptive Graph Learning—0
Graph-Level Embedding for Time-Evolving Graphs—0
CoarSAS2hvec: Heterogeneous Information Network Embedding with Balanced Network Sampling—0
Heterogeneous Information Network Embedding for Meta Path based Proximity—0
Data-driven biological network alignment that uses topological, sequence, and functional information—0
Integrated Node Encoder for Labelled Textual Networks—0
EPNE: Evolutionary Pattern Preserving Network Embedding—0
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