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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 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
Fast Sequence Based Embedding with Diffusion GraphsCode1
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
Demographic Inference on Twitter using Recursive Neural Networks—0
CANE: Context-Aware Network Embedding for Relation ModelingCode0
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
Video Tracking Using Learned Hierarchical Features—0
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