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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
Multi-View Collaborative Network EmbeddingCode1
Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic GraphsCode1
Fast Network Embedding Enhancement via High Order Proximity ApproximationCode1
Machine Learning on Graphs: A Model and Comprehensive TaxonomyCode1
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
Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNNCode1
Heterogeneous Network Representation Learning: A Unified Framework with Survey and BenchmarkCode1
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
Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress?Code0
A Node Embedding Framework for Integration of Similarity-based Drug Combination Prediction—0
Using Distributional Thesaurus Embedding for Co-hyponymy Detection—0
FONDUE: A Framework for Node Disambiguation Using Network Embeddings—0
Adaptive Graph Auto-Encoder for General Data ClusteringCode1
Adversarial Deep Network Embedding for Cross-network Node ClassificationCode1
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
LouvainNE: Hierarchical Louvain Method for High Quality and Scalable Network EmbeddingCode1
Data-driven biological network alignment that uses topological, sequence, and functional information—0
Fast Sequence-Based Embedding with Diffusion GraphsCode1
Document Network Projection in Pretrained Word Embedding Space—0
Inductive Document Network Embedding with Topic-Word AttentionCode1
A Block-based Generative Model for Attributed Networks Embedding—0
Large-scale Gender/Age Prediction of Tumblr Users—0
Deep Learning for Learning Graph Representations—0
LouvainNE: Hierarchical Louvain Method for High Quality and Scalable Network Embedding.Code1
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
BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network—0
Network Embedding: An Overview—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
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