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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 41–50 of 403 papers

TitleStatusHype
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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