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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 326–350 of 403 papers

TitleStatusHype
Enhanced Network Embedding with Text InformationCode0
Attributed Network Embedding for Incomplete Attributed NetworksCode0
Flexible Attributed Network EmbeddingCode0
MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional NetworksCode0
SepNE: Bringing Separability to Network Embedding—0
Streaming Network Embedding through Local Actions—0
Multi-View Network Embedding Via Graph Factorization Clustering and Co-Regularized Multi-View Agreement—0
Binarized Attributed Network EmbeddingCode0
Attention Models with Random Features for Multi-layered Graph Embeddings—0
Improved Deep Embeddings for Inferencing with Multi-Layered Networks—0
Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks—0
Learning Embeddings of Directed Networks with Text-Associated Nodes---with Applications in Software Package Dependency Networks—0
BiasedWalk: Biased Sampling for Representation Learning on GraphsCode0
Deep Feature Learning of Multi-Network Topology for Node Classification—0
Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment—0
Multi-Level Network Embedding with Boosted Low-Rank Matrix ApproximationCode0
Interaction-Aware Topic Model for Microblog Conversations through Network Embedding and User Attention—0
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask DependenciesCode0
NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic NetworksCode0
Learning Deep Network Representations with Adversarially Regularized AutoencodersCode0
Efficient Training on Very Large Corpora via Gramian Estimation—0
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information NetworksCode0
Resource-Efficient Neural Architect—0
Spectral Network Embedding: A Fast and Scalable Method via Sparsity—0
struc2gauss: Structural Role Preserving Network Embedding via Gaussian Embedding—0
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