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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 101–150 of 403 papers

TitleStatusHype
DeHIN: A Decentralized Framework for Embedding Large-scale Heterogeneous Information Networks—0
Demographic Inference on Twitter using Recursive Neural Networks—0
Hierarchical Graph Neural Networks—0
Detecting Online Hate Speech: Approaches Using Weak Supervision and Network Embedding Models—0
Diffusion Based Network Embedding—0
Diffusion Maps for Textual Network Embedding—0
DINE: A Framework for Deep Incomplete Network Embedding—0
DISCO: Influence Maximization Meets Network Embedding and Deep Learning—0
Data-driven biological network alignment that uses topological, sequence, and functional information—0
Document Network Embedding: Coping for Missing Content and Missing Links—0
Heterogeneous Information Network Embedding for Meta Path based Proximity—0
BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network—0
A Multi-Domain VNE Algorithm based on Load Balancing in the IoT networks—0
Dynamic Graph Embedding via LSTM History Tracking—0
Dynamic Network Embeddings for Network Evolution Analysis—0
Dynamic Network Embedding Survey—0
Big Networks: A Survey—0
Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning—0
dynnode2vec: Scalable Dynamic Network Embedding—0
AHINE: Adaptive Heterogeneous Information Network Embedding—0
A Block-based Generative Model for Attributed Networks Embedding—0
Associative Learning for Network Embedding—0
Heterogeneous Federated Learning Systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism—0
Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks—0
Efficient Inner Product Approximation in Hybrid Spaces—0
Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling—0
Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search—0
AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks—0
Cross Version Defect Prediction with Class Dependency Embeddings—0
Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments—0
COSINE: Compressive Network Embedding on Large-scale Information Networks—0
ANAE: Learning Node Context Representation for Attributed Network Embedding—0
Heterogeneous Edge Embeddings for Friend Recommendation—0
High-order joint embedding for multi-level link prediction—0
Controlled Deep Reinforcement Learning for Optimized Slice Placement—0
A General Framework for Content-enhanced Network Representation Learning—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
Grammar-Based Grounded Lexicon Learning—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
Genome Sequence Classification for Animal Diagnostics with Graph Representations and Deep Neural Networks—0
Community Aware Random Walk for Network Embedding—0
ActiveHNE: Active Heterogeneous Network Embedding—0
Learning Features of Network Structures Using Graphlets—0
Unsupervised Graph Embedding via Adaptive Graph 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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