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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 101–150 of 403 papers

TitleStatusHype
Cross-Network Social User Embedding with Hybrid Differential Privacy GuaranteesCode0
Network Representation Learning with Rich Text InformationCode0
metapath2vec: Scalable Representation Learning for Heterogeneous NetworksCode0
ASD Classification on Dynamic Brain Connectome using Temporal Random Walk with Transformer-based Dynamic Network EmbeddingCode0
Contextual Regression: An Accurate and Conveniently Interpretable Nonlinear Model for Mining Discovery from Scientific DataCode0
Non-Euclidean Mixture Model for Social Network EmbeddingCode0
Learning multi-resolution representations of research patterns in bibliographic networksCode0
PRUNE: Preserving Proximity and Global Ranking for Network EmbeddingCode0
LEAP nets for power grid perturbationsCode0
BHGNN-RT: Network embedding for directed heterogeneous graphsCode0
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to MillisecondsCode0
Learning Deep Network Representations with Adversarially Regularized AutoencodersCode0
DyCSC: Modeling the Evolutionary Process of Dynamic Networks Based on Cluster StructureCode0
BiasedWalk: Biased Sampling for Representation Learning on GraphsCode0
Search Efficient Binary Network EmbeddingCode0
Shapes as Product Differentiation: Neural Network Embedding in the Analysis of Markets for FontsCode0
Learning Role-based Graph EmbeddingsCode0
JNET: Learning User Representations via Joint Network Embedding and Topic EmbeddingCode0
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
Collaborative Graph Neural Networks for Attributed Network EmbeddingCode0
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information NetworksCode0
Edgeless-GNN: Unsupervised Representation Learning for Edgeless NodesCode0
Is a Single Vector Enough? Exploring Node Polysemy for Network EmbeddingCode0
L2G2G: a Scalable Local-to-Global Network Embedding with Graph AutoencodersCode0
Boosting House Price Predictions using Geo-Spatial Network EmbeddingCode0
Efficient Network Embedding by Approximate Equitable PartitionsCode0
Improving Textual Network Learning with Variational Homophilic EmbeddingsCode0
A novel robust integrating method by high-order proximity for self-supervised attribute network embeddingCode0
Integrating Network Embedding and Community Outlier Detection via Multiclass Graph DescriptionCode0
H^2TNE: Temporal Heterogeneous Information Network Embedding in Hyperbolic SpacesCode0
Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization PerspectiveCode0
GraphVite: A High-Performance CPU-GPU Hybrid System for Node EmbeddingCode0
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask DependenciesCode0
IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for RecommendationCode0
Enhanced Network Embedding with Text InformationCode0
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network EmbeddingCode0
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data ManifoldsCode0
HAHE: Hierarchical Attentive Heterogeneous Information Network EmbeddingCode0
GENE: Global Event Network EmbeddingCode0
Global Vectors for Node RepresentationsCode0
Gradient-Based Spectral Embeddings of Random Dot Product GraphsCode0
Exploiting Node Content for Multiview Graph Convolutional Network and Adversarial RegularizationCode0
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information NetworksCode0
CANE: Context-Aware Network Embedding for Relation ModelingCode0
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money LaunderingCode0
Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node DescriptorsCode0
Font Size: Community Preserving Network EmbeddingCode0
Dynamic Embedding on Textual Networks via a Gaussian ProcessCode0
Flexible Attributed Network EmbeddingCode0
Graph Representation Learning via Hard and Channel-Wise Attention NetworksCode0
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