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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
Deep Kernel Supervised Hashing for Node Classification in Structural Networks—0
Attributed Network Embedding for Learning in a Dynamic Environment—0
Initialization for Network Embedding: A Graph Partition Approach—0
Efficient Inner Product Approximation in Hybrid Spaces—0
End-to-End triplet loss based fine-tuning for network embedding in effective PII detection—0
Aligning context-based statistical models of language with brain activity during reading—0
ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions—0
Dynamic Network Embeddings for Network Evolution Analysis—0
Attention Models with Random Features for Multi-layered Graph Embeddings—0
Deep Coevolutionary Network: Embedding User and Item Features for Recommendation—0
Deep Adversarial Network Alignment—0
A Survey on Signed Graph Embedding: Methods and Applications—0
Dynamic Network Embedding Survey—0
Deep Contrastive Multiview Network Embedding—0
Deep Feature Learning of Multi-Network Topology for Node Classification—0
Attribute2vec: Deep Network Embedding Through Multi-Filtering GCN—0
Learning to Embed Categorical Features without Embedding Tables for Recommendation—0
DeepHE: Accurately Predicting Human Essential Genes based on Deep Learning—0
Deep Hashing for Signed Social Network Embedding—0
Data-driven biological network alignment that uses topological, sequence, and functional information—0
Deep Learning for Learning Graph Representations—0
Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes—0
Aligning Users Across Social Networks Using Network Embedding—0
Deep Partial Multiplex Network Embedding—0
Document Network Embedding: Coping for Missing Content and Missing Links—0
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