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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 326–350 of 403 papers

TitleStatusHype
Network Embedding: on Compression and LearningCode0
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask DependenciesCode0
A novel robust integrating method by high-order proximity for self-supervised attribute network embeddingCode0
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted NetworksCode0
Efficient Network Embedding by Approximate Equitable PartitionsCode0
SCE: Scalable Network Embedding from Sparsest CutCode0
Boosting House Price Predictions using Geo-Spatial Network EmbeddingCode0
Attributed Network Embedding for Incomplete Attributed NetworksCode0
Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress?Code0
Search Efficient Binary Network EmbeddingCode0
Network Representation Learning with Rich Text InformationCode0
Edgeless-GNN: Unsupervised Representation Learning for Edgeless NodesCode0
Adversarial Attack on Network Embeddings via Supervised Network PoisoningCode0
BlueTempNet: A Temporal Multi-network Dataset of Social Interactions in Bluesky SocialCode0
Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network EmbeddingCode0
Neural Embedding Propagation on Heterogeneous NetworksCode0
Improving Textual Network Learning with Variational Homophilic EmbeddingsCode0
Trustworthiness-Driven Graph Convolutional Networks for Signed Network EmbeddingCode0
New Datasets and a Benchmark of Document Network Embedding Methods for Scientific Expert FindingCode0
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information NetworksCode0
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to MillisecondsCode0
Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author IdentificationCode0
Integrating Network Embedding and Community Outlier Detection via Multiclass Graph DescriptionCode0
IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for RecommendationCode0
Binarized Attributed Network EmbeddingCode0
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