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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 76–100 of 403 papers

TitleStatusHype
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
Fusion of Minutia Cylinder Codes and Minutia Patch Embeddings for Latent Fingerprint Recognition—0
Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model—0
VN Network: Embedding Newly Emerging Entities with Virtual Neighbors—0
Loss Landscape of Shallow ReLU-like Neural Networks: Stationary Points, Saddle Escaping, and Network Embedding—0
L2G2G: a Scalable Local-to-Global Network Embedding with Graph AutoencodersCode0
Detecting local perturbations of networks in a latent hyperbolic embedding space—0
Clustering Molecular Energy Landscapes by Adaptive Network Embedding—0
BHGNN-RT: Network embedding for directed heterogeneous graphsCode0
Hedging carbon risk with a network approach—0
Semantic Annotation of Tabular Data for Machine-to-Machine Interoperability via Neuro-Symbolic Anchoring—0
A Simple and Powerful Framework for Stable Dynamic Network EmbeddingCode0
Trustworthiness-Driven Graph Convolutional Networks for Signed Network EmbeddingCode0
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted NetworksCode0
Network Embedding Using Sparse Approximations of Random Walks—0
A Weakly Supervised Segmentation Network Embedding Cross-scale Attention Guidance and Noise-sensitive Constraint for Detecting Tertiary Lymphoid Structures of Pancreatic Tumors—0
Gradient-Based Spectral Embeddings of Random Dot Product GraphsCode0
Collaborative Graph Neural Networks for Attributed Network EmbeddingCode0
Source-Aware Embedding Training on Heterogeneous Information Networks—0
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to MillisecondsCode0
Graph-Level Embedding for Time-Evolving Graphs—0
Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction—0
Semantic Random Walk for Graph Representation Learning in Attributed Graphs—0
Zoo Guide to Network Embedding—0
H^2TNE: Temporal Heterogeneous Information Network Embedding in Hyperbolic SpacesCode0
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