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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 126–150 of 403 papers

TitleStatusHype
Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment—0
AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks—0
Cross Version Defect Prediction with Class Dependency Embeddings—0
Graph-Level Embedding for Time-Evolving Graphs—0
COSINE: Compressive Network Embedding on Large-scale Information Networks—0
ANAE: Learning Node Context Representation for Attributed Network Embedding—0
Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments—0
Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search—0
Hierarchical Graph Neural Networks—0
Controlled Deep Reinforcement Learning for Optimized Slice Placement—0
A General Framework for Content-enhanced Network Representation Learning—0
Learning Features of Network Structures Using Graphlets—0
Exact Recovery of Community Structures Using DeepWalk and Node2vec—0
Compositional Network Embedding—0
ASBERT: Siamese and Triplet network embedding for open question answering—0
A novel stochastic model based on echo state networks for hydrological time series forecasting—0
Community detection using low-dimensional network embedding algorithms—0
GANE: A Generative Adversarial Network Embedding—0
Community Aware Random Walk for Network Embedding—0
ActiveHNE: Active Heterogeneous Network Embedding—0
Genome Sequence Classification for Animal Diagnostics with Graph Representations and Deep Neural Networks—0
Grammar-Based Grounded Lexicon Learning—0
Collaborative filtering via heterogeneous neural networks—0
Complex Network Classification with Convolutional Neural Network—0
Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation—0
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