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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 151–200 of 403 papers

TitleStatusHype
An Isolation-Aware Online Virtual Network Embedding via Deep Reinforcement Learning—0
On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications—0
Full-Network Embedding in a Multimodal Embedding Pipeline—0
Exact Recovery of Community Structures Using DeepWalk and Node2vec—0
Fusion of Minutia Cylinder Codes and Minutia Patch Embeddings for Latent Fingerprint Recognition—0
GAHNE: Graph-Aggregated Heterogeneous Network Embedding—0
GANE: A Generative Adversarial Network Embedding—0
Controlled Deep Reinforcement Learning for Optimized Slice Placement—0
Large-Scale Network Embedding in Apache Spark—0
ANAE: Learning Node Context Representation for Attributed Network Embedding—0
Genome Sequence Classification for Animal Diagnostics with Graph Representations and Deep Neural Networks—0
COSINE: Compressive Network Embedding on Large-scale Information Networks—0
Learning Features of Network Structures Using Graphlets—0
Cross Version Defect Prediction with Class Dependency Embeddings—0
Collaborative filtering via heterogeneous neural networks—0
AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks—0
Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation—0
Grammar-Based Grounded Lexicon Learning—0
Unsupervised Graph Embedding via Adaptive Graph Learning—0
Graph-Level Embedding for Time-Evolving Graphs—0
An Out-of-the-box Full-network Embedding for Convolutional Neural Networks—0
EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction—0
Equivalence between LINE and Matrix Factorization—0
CoarSAS2hvec: Heterogeneous Information Network Embedding with Balanced Network Sampling—0
Integrated Node Encoder for Labelled Textual Networks—0
EPNE: Evolutionary Pattern Preserving Network Embedding—0
Heterogeneous Federated Learning Systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism—0
Heterogeneous Information Network Embedding for Meta Path based Proximity—0
Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search—0
EPINE: Enhanced Proximity Information Network Embedding—0
HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding—0
Hierarchical Graph Neural Networks—0
CoANE: Modeling Context Co-occurrence for Attributed Network Embedding—0
High-order joint embedding for multi-level link prediction—0
High Tension Lines: Predicting robustness of high-voltage power-grids to cascading failure using network embedding—0
EPARS: Early Prediction of At-risk Students with Online and Offline Learning Behaviors—0
Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank—0
HONEM: Learning Embedding for Higher Order Networks—0
Hyperbolic Multiplex Network Embedding with Maps of Random Walk—0
Hyperbolic Node Embedding for Signed Networks—0
Clustering Molecular Energy Landscapes by Adaptive Network Embedding—0
Identity-sensitive Word Embedding through Heterogeneous Networks—0
PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding—0
Improved Deep Embeddings for Inferencing with Multi-Layered Networks—0
Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment—0
Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling—0
Improving Textual Network Embedding with Global Attention via Optimal Transport—0
Aligning Users Across Social Networks Using Network Embedding—0
Network embedding unveils the hidden interactions in the mammalian virome—0
Interaction-Aware Topic Model for Microblog Conversations through Network Embedding and User Attention—0
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