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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 251–300 of 403 papers

TitleStatusHype
BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network—0
Time-aware Gradient Attack on Dynamic Network Link Prediction—0
Hyperbolic Multiplex Network Embedding with Maps of Random Walk—0
RWNE: A Scalable Random-Walk-Based Network Embedding Framework with Personalized Higher-Order Proximity PreservedCode0
Unsupervised Attributed Multiplex Network EmbeddingCode0
On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network—0
weg2vec: Event embedding for temporal networks—0
Dynamic Graph Embedding via LSTM History Tracking—0
News2vec: News Network Embedding with Subnode Information—0
Hyperbolic Node Embedding for Signed Networks—0
Network2Vec Learning Node Representation Based on Space Mapping in Networks—0
Tutorial on NLP-Inspired Network Embedding—0
RiWalk: Fast Structural Node Embedding via Role IdentificationCode0
Dynamic Embedding on Textual Networks via a Gaussian ProcessCode0
Improving Textual Network Learning with Variational Homophilic EmbeddingsCode0
Neural Embedding Propagation on Heterogeneous NetworksCode0
Multi-scale Attributed Node EmbeddingCode0
Selective Network Discovery via Deep Reinforcement Learning on Embedded Spaces—0
Temporal Network Embedding with Micro- and Macro-dynamicsCode0
HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding—0
Initialization for Network Embedding: A Graph Partition Approach—0
NETR-Tree: An Eifficient Framework for Social-Based Time-Aware Spatial Keyword Query—0
On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications—0
LEAP nets for power grid perturbationsCode0
MEGAN: A Generative Adversarial Network for Multi-View Network Embedding—0
AHINE: Adaptive Heterogeneous Information Network Embedding—0
Domain-adversarial Network AlignmentCode0
HONEM: Learning Embedding for Higher Order Networks—0
Deep Hashing for Signed Social Network Embedding—0
ProNE: Fast and Scalable Network Representation LearningCode0
IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for RecommendationCode0
Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling—0
PathRank: A Multi-Task Learning Framework to Rank Paths in Spatial Networks—0
Graph Representation Learning via Hard and Channel-Wise Attention NetworksCode0
Network Embedding: on Compression and LearningCode0
NetSMF: Large-Scale Network Embedding as Sparse Matrix FactorizationCode0
Dynamic Network Embeddings for Network Evolution Analysis—0
ANAE: Learning Node Context Representation for Attributed Network Embedding—0
DISCO: Influence Maximization Meets Network Embedding and Deep Learning—0
Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank—0
Network Representation of Large-Scale Heterogeneous RNA Sequences with Integration of Diverse Multi-omics, Interactions, and Annotations Data—0
Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node DescriptorsCode0
Dynamic Network Embedding via Incremental Skip-gram with Negative SamplingCode0
Improving Textual Network Embedding with Global Attention via Optimal Transport—0
Task-Guided Pair Embedding in Heterogeneous NetworkCode0
Is a Single Vector Enough? Exploring Node Polysemy for Network EmbeddingCode0
Relation Structure-Aware Heterogeneous Information Network Embedding—0
ActiveHNE: Active Heterogeneous Network Embedding—0
Physiological Signal Embeddings (PHASE) via Interpretable Stacked Models—0
ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions—0
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