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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 351–375 of 403 papers

TitleStatusHype
Dynamic Network Embedding via Incremental Skip-gram with Negative SamplingCode0
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph EmbeddingCode0
Is a Single Vector Enough? Exploring Node Polysemy for Network EmbeddingCode0
Non-Euclidean Mixture Model for Social Network EmbeddingCode0
Task-Guided Pair Embedding in Heterogeneous NetworkCode0
JNET: Learning User Representations via Joint Network Embedding and Topic EmbeddingCode0
DyCSC: Modeling the Evolutionary Process of Dynamic Networks Based on Cluster StructureCode0
Billion-scale Network Embedding with Iterative Random ProjectionCode0
Shapes as Product Differentiation: Neural Network Embedding in the Analysis of Markets for FontsCode0
L2G2G: a Scalable Local-to-Global Network Embedding with Graph AutoencodersCode0
BiasedWalk: Biased Sampling for Representation Learning on GraphsCode0
SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link PredictionCode0
A Simple and Powerful Framework for Stable Dynamic Network EmbeddingCode0
ASD Classification on Dynamic Brain Connectome using Temporal Random Walk with Transformer-based Dynamic Network EmbeddingCode0
TempNodeEmb:Temporal Node Embedding considering temporal edge influence matrixCode0
LEAP nets for power grid perturbationsCode0
Temporal Graph Network Embedding with Causal Anonymous Walks RepresentationsCode0
Unsupervised Network Embedding for Graph Visualization, Clustering and ClassificationCode0
Learning Deep Network Representations with Adversarially Regularized AutoencodersCode0
Temporal Network Embedding with Micro- and Macro-dynamicsCode0
Temporal Network Representation Learning via Historical Neighborhoods AggregationCode0
ProNE: Fast and Scalable Network Representation LearningCode0
PRUNE: Preserving Proximity and Global Ranking for Network EmbeddingCode0
Learning multi-resolution representations of research patterns in bibliographic networksCode0
Learning Role-based Graph EmbeddingsCode0
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