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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 351–400 of 403 papers

TitleStatusHype
Diffusion Maps for Textual Network Embedding—0
GANE: A Generative Adversarial Network Embedding—0
Diffusion Based Network Embedding—0
Billion-scale Network Embedding with Iterative Random ProjectionCode0
RSDNE: Exploring Relaxed Similarity and Dissimilarity from Completely-imbalanced Labels for Network EmbeddingCode0
Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network EmbeddingCode0
Scalable attribute-aware network embedding with locality—0
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information NetworksCode0
Learning Depth from Single Images with Deep Neural Network Embedding Focal Length—0
AAANE: Attention-based Adversarial Autoencoder for Multi-scale Network Embedding—0
Broad Learning for Healthcare—0
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network EmbeddingCode0
AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks—0
Range-Only Localization in n-Dimensional Networks With Arbitrary Anchor Placement—0
Can Network Embedding of Distributional Thesaurus be Combined with Word Vectors for Better Representation?—0
Learning Role-based Graph EmbeddingsCode0
Complex Network Classification with Convolutional Neural Network—0
mvn2vec: Preservation and Collaboration in Multi-View Network EmbeddingCode0
Learning Document Embeddings With CNNs—0
SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link PredictionCode0
PRUNE: Preserving Proximity and Global Ranking for Network EmbeddingCode0
Representation Learning for Scale-free Networks—0
Adversarial Network Embedding—0
Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization PerspectiveCode0
Vertex-Context Sampling for Weighted Network Embedding—0
Contextual Regression: An Accurate and Conveniently Interpretable Nonlinear Model for Mining Discovery from Scientific DataCode0
Community Aware Random Walk for Network Embedding—0
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vecCode0
metapath2vec: Scalable Representation Learning for Heterogeneous NetworksCode0
Full-Network Embedding in a Multimodal Embedding Pipeline—0
Equivalence between LINE and Matrix Factorization—0
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via RankingCode0
CANE: Context-Aware Network Embedding for Relation ModelingCode0
Demographic Inference on Twitter using Recursive Neural Networks—0
Attributed Network Embedding for Learning in a Dynamic Environment—0
An Out-of-the-box Full-network Embedding for Convolutional Neural Networks—0
struc2vec: Learning Node Representations from Structural IdentityCode0
Bib2vec: Embedding-based Search System for Bibliographic Information—0
Distributed Representations of Signed Networks—0
Font Size: Community Preserving Network EmbeddingCode0
Name Disambiguation in Anonymized Graphs using Network EmbeddingCode0
Heterogeneous Information Network Embedding for Meta Path based Proximity—0
Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author IdentificationCode0
Pairwise FastText Classifier for Entity Disambiguation—0
Identity-sensitive Word Embedding through Heterogeneous Networks—0
A General Framework for Content-enhanced Network Representation Learning—0
Deep Coevolutionary Network: Embedding User and Item Features for Recommendation—0
Predict Anchor Links across Social Networks via an Embedding Approach—0
Aligning Users Across Social Networks Using Network Embedding—0
Structural Deep Network EmbeddingCode0
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