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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 276–300 of 403 papers

TitleStatusHype
Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search—0
HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding—0
Hierarchical Graph Neural Networks—0
High-order joint embedding for multi-level link prediction—0
High Tension Lines: Predicting robustness of high-voltage power-grids to cascading failure using network embedding—0
Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank—0
HONEM: Learning Embedding for Higher Order Networks—0
Hyperbolic Multiplex Network Embedding with Maps of Random Walk—0
Hyperbolic Node Embedding for Signed Networks—0
Identifying Transition States of Chemical Kinetic Systems using Network Embedding Techniques—0
Identity-sensitive Word Embedding through Heterogeneous Networks—0
Improved Deep Embeddings for Inferencing with Multi-Layered Networks—0
Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment—0
Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling—0
Improving Textual Network Embedding with Global Attention via Optimal Transport—0
Exploiting Node Content for Multiview Graph Convolutional Network and Adversarial RegularizationCode0
Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization PerspectiveCode0
Flexible Attributed Network EmbeddingCode0
Collaborative Graph Neural Networks for Attributed Network EmbeddingCode0
Font Size: Community Preserving Network EmbeddingCode0
Adversarial network embedding with bootstrapped representations for sparse networksCode0
Representation Learning on Heterostructures via Heterogeneous Anonymous WalksCode0
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information NetworksCode0
Unsupervised Attributed Multiplex Network EmbeddingCode0
CANE: Context-Aware Network Embedding for Relation ModelingCode0
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