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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 271–280 of 403 papers

TitleStatusHype
Graph-Level Embedding for Time-Evolving Graphs—0
Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments—0
Heterogeneous Edge Embeddings for Friend Recommendation—0
Heterogeneous Federated Learning Systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism—0
Heterogeneous Information Network Embedding for Meta Path based Proximity—0
Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search—0
HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding—0
Hierarchical Graph Neural Networks—0
High-order joint embedding for multi-level link prediction—0
High Tension Lines: Predicting robustness of high-voltage power-grids to cascading failure using network embedding—0
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