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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 51–100 of 403 papers

TitleStatusHype
Adversarial Deep Network Embedding for Cross-network Node ClassificationCode1
BlueTempNet: A Temporal Multi-network Dataset of Social Interactions in Bluesky SocialCode0
Adversarial network embedding with bootstrapped representations for sparse networksCode0
mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network EmbeddingCode0
metapath2vec: Scalable Representation Learning for Heterogeneous NetworksCode0
BiasedWalk: Biased Sampling for Representation Learning on GraphsCode0
MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional NetworksCode0
BHGNN-RT: Network embedding for directed heterogeneous graphsCode0
Billion-scale Network Embedding with Iterative Random ProjectionCode0
Binarized Attributed Network EmbeddingCode0
Learning Vertex Representations for Bipartite NetworksCode0
Boosting House Price Predictions using Geo-Spatial Network EmbeddingCode0
Learning multi-resolution representations of research patterns in bibliographic networksCode0
Learning Role-based Graph EmbeddingsCode0
L2G2G: a Scalable Local-to-Global Network Embedding with Graph AutoencodersCode0
Attributed Network Embedding via Subspace DiscoveryCode0
LEAP nets for power grid perturbationsCode0
Is a Single Vector Enough? Exploring Node Polysemy for Network EmbeddingCode0
Integrating Network Embedding and Community Outlier Detection via Multiclass Graph DescriptionCode0
JNET: Learning User Representations via Joint Network Embedding and Topic EmbeddingCode0
Learning Deep Network Representations with Adversarially Regularized AutoencodersCode0
LNEMLC: Label Network Embeddings for Multi-Label ClassificationCode0
Multi-Level Network Embedding with Boosted Low-Rank Matrix ApproximationCode0
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask DependenciesCode0
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via RankingCode0
HAHE: Hierarchical Attentive Heterogeneous Information Network EmbeddingCode0
GraphVite: A High-Performance CPU-GPU Hybrid System for Node EmbeddingCode0
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network EmbeddingCode0
H^2TNE: Temporal Heterogeneous Information Network Embedding in Hyperbolic SpacesCode0
GENE: Global Event Network EmbeddingCode0
Data driven approximation of parametrized PDEs by Reduced Basis and Neural NetworksCode0
DANES: Deep Neural Network Ensemble Architecture for Social and Textual Context-aware Fake News DetectionCode0
Adversarial Attack on Network Embeddings via Supervised Network PoisoningCode0
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data ManifoldsCode0
CSNE: Conditional Signed Network EmbeddingCode0
DeBayes: a Bayesian Method for Debiasing Network EmbeddingsCode0
A Simple and Powerful Framework for Stable Dynamic Network EmbeddingCode0
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money LaunderingCode0
Gradient-Based Spectral Embeddings of Random Dot Product GraphsCode0
Graph Representation Learning via Hard and Channel-Wise Attention NetworksCode0
Cross-Network Social User Embedding with Hybrid Differential Privacy GuaranteesCode0
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted NetworksCode0
Attributed Network Embedding for Incomplete Attributed NetworksCode0
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information NetworksCode0
Dynamic Embedding on Textual Networks via a Gaussian ProcessCode0
IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for RecommendationCode0
Deep Network Embedding for Graph Representation Learning in Signed NetworksCode0
Deep Node Ranking for Neuro-symbolic Structural Node Embedding and ClassificationCode0
Global Vectors for Node RepresentationsCode0
Improving Textual Network Learning with Variational Homophilic EmbeddingsCode0
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