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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

Showing 3140 of 403 papers

TitleStatusHype
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
Heterogeneous Network Representation Learning: A Unified Framework with Survey and BenchmarkCode1
Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNNCode1
Adaptive Graph Auto-Encoder for General Data ClusteringCode1
Adversarial Deep Network Embedding for Cross-network Node ClassificationCode1
LouvainNE: Hierarchical Louvain Method for High Quality and Scalable Network EmbeddingCode1
Fast Sequence-Based Embedding with Diffusion GraphsCode1
Inductive Document Network Embedding with Topic-Word AttentionCode1
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