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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 201–250 of 403 papers

TitleStatusHype
Learning to Embed Categorical Features without Embedding Tables for Recommendation—0
TriNE: Network Representation Learning for Tripartite Heterogeneous Networks—0
Inductive Graph Embeddings through Locality Encodings—0
EPNE: Evolutionary Pattern Preserving Network Embedding—0
Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation—0
Learning a Deep Part-based Representation by Preserving Data Distribution—0
Layer-stacked Attention for Heterogeneous Network Embedding—0
Boosting House Price Predictions using Geo-Spatial Network EmbeddingCode0
TempNodeEmb:Temporal Node Embedding considering temporal edge influence matrixCode0
Random Walks: A Review of Algorithms and Applications—0
Big Networks: A Survey—0
DINE: A Framework for Deep Incomplete Network Embedding—0
Detecting Online Hate Speech: Approaches Using Weak Supervision and Network Embedding Models—0
Genome Sequence Classification for Animal Diagnostics with Graph Representations and Deep Neural Networks—0
Integrating Network Embedding and Community Outlier Detection via Multiclass Graph DescriptionCode0
A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning—0
Next Waves in Veridical Network Embedding—0
SCE: Scalable Network Embedding from Sparsest CutCode0
Online Dynamic Network Embedding—0
EPARS: Early Prediction of At-risk Students with Online and Offline Learning Behaviors—0
Integrated Node Encoder for Labelled Textual Networks—0
CSNE: Conditional Signed Network EmbeddingCode0
Network Embedding Using Deep Robust Nonnegative Matrix Factorization—0
New Datasets and a Benchmark of Document Network Embedding Methods for Scientific Expert FindingCode0
Attribute2vec: Deep Network Embedding Through Multi-Filtering GCN—0
Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering—0
Temporal Network Representation Learning via Historical Neighborhoods AggregationCode0
RNE: A Scalable Network Embedding for Billion-scale Recommendation—0
Unsupervised Graph Embedding via Adaptive Graph Learning—0
EPINE: Enhanced Proximity Information Network Embedding—0
DeBayes: a Bayesian Method for Debiasing Network EmbeddingsCode0
A Node Embedding Framework for Integration of Similarity-based Drug Combination Prediction—0
Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress?Code0
Using Distributional Thesaurus Embedding for Co-hyponymy Detection—0
FONDUE: A Framework for Node Disambiguation Using Network Embeddings—0
DeepHE: Accurately Predicting Human Essential Genes based on Deep Learning—0
Vertex-reinforced Random Walk for Network EmbeddingCode0
ALPINE: Active Link Prediction using Network Embedding—0
Data-driven biological network alignment that uses topological, sequence, and functional information—0
Document Network Projection in Pretrained Word Embedding Space—0
A Block-based Generative Model for Attributed Networks Embedding—0
Deep Learning for Learning Graph Representations—0
Large-scale Gender/Age Prediction of Tumblr Users—0
A Non-negative Symmetric Encoder-Decoder Approach for Community DetectionCode0
Privacy Attacks on Network Embeddings—0
Beyond Node Embedding: A Direct Unsupervised Edge Representation Framework for Homogeneous Networks—0
Document Network Embedding: Coping for Missing Content and Missing Links—0
JNET: Learning User Representations via Joint Network Embedding and Topic EmbeddingCode0
MANELA: A Multi-Agent Algorithm for Learning Network Embeddings—0
Network Embedding: An Overview—0
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