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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 126–150 of 403 papers

TitleStatusHype
Large-Scale Network Embedding in Apache Spark—0
Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path—0
CoANE: Modeling Context Co-occurrence for Attributed Network Embedding—0
Relation order histograms as a network embedding toolCode0
ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph NetworksCode1
GENE: Global Event Network EmbeddingCode0
Network embedding unveils the hidden interactions in the mammalian virome—0
Robust Dynamic Network Embedding via EnsemblesCode1
High Tension Lines: Predicting robustness of high-voltage power-grids to cascading failure using network embedding—0
Where are we in embedding spaces? A Comprehensive Analysis on Network Embedding Approaches for Recommender SystemsCode0
Multi-Aspect Temporal Network Embedding: A Mixture of Hawkes Process View—0
Independent Asymmetric Embedding for Information Diffusion Prediction on Social Networks—0
Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective—0
An Influence-based Approach for Root Cause Alarm Discovery in Telecom NetworksCode1
Hierarchical Graph Neural Networks—0
MUSE: Multi-faceted Attention for Signed Network Embedding—0
Network Embedding via Deep Prediction Model—0
Mutual Contrastive Learning for Visual Representation LearningCode1
ASBERT: Siamese and Triplet network embedding for open question answering—0
Edgeless-GNN: Unsupervised Representation Learning for Edgeless NodesCode0
mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network EmbeddingCode0
Dynamic Network Embedding Survey—0
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph EmbeddingCode0
Progresses and Challenges in Link Prediction—0
Fast Graph Learning with Unique Optimal SolutionsCode1
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