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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 301–350 of 403 papers

TitleStatusHype
Multimodal Deep Network Embedding with Integrated Structure and Attribute Information—0
Subgraph Networks with Application to Structural Feature Space Expansion—0
Efficient Inner Product Approximation in Hybrid Spaces—0
Splitter: Learning Node Representations that Capture Multiple Social ContextsCode0
Multi-Hot Compact Network Embedding—0
EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction—0
GraphVite: A High-Performance CPU-GPU Hybrid System for Node EmbeddingCode0
Representation Learning for Recommender Systems with Application to the Scientific Literature—0
Global Vectors for Node RepresentationsCode0
Deep Adversarial Network Alignment—0
Unsupervised Network Embedding for Graph Visualization, Clustering and ClassificationCode0
Deep Node Ranking for Neuro-symbolic Structural Node Embedding and ClassificationCode0
Heterogeneous Edge Embeddings for Friend Recommendation—0
HAHE: Hierarchical Attentive Heterogeneous Information Network EmbeddingCode0
Learning Vertex Representations for Bipartite NetworksCode0
Attributed Network Embedding via Subspace DiscoveryCode0
Search Efficient Binary Network EmbeddingCode0
Deep Network Embedding for Graph Representation Learning in Signed NetworksCode0
COSINE: Compressive Network Embedding on Large-scale Information Networks—0
Learning Features of Network Structures Using Graphlets—0
LNEMLC: Label Network Embeddings for Multi-Label ClassificationCode0
dynnode2vec: Scalable Dynamic Network Embedding—0
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
Outlier Aware Network Embedding for Attributed NetworksCode1
Streaming Network Embedding through Local Actions—0
SepNE: Bringing Separability to Network Embedding—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
BiasedWalk: Biased Sampling for Representation Learning on GraphsCode0
Deep Feature Learning of Multi-Network Topology for Node Classification—0
Learning Embeddings of Directed Networks with Text-Associated Nodes---with Applications in Software Package Dependency Networks—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
Learning Deep Network Representations with Adversarially Regularized AutoencodersCode0
NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic NetworksCode0
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask DependenciesCode0
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 SparsityCode0
struc2gauss: Structural Role Preserving Network Embedding via Gaussian Embedding—0
Diffusion Maps for Textual Network Embedding—0
GANE: A Generative Adversarial Network Embedding—0
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