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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 26–50 of 403 papers

TitleStatusHype
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource AllocationCode2
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
Random Walk on Multiple NetworksCode1
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
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