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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 201–210 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
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