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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 21–30 of 403 papers

TitleStatusHype
BlueTempNet: A Temporal Multi-network Dataset of Social Interactions in Bluesky SocialCode0
Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement LearningCode2
Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments—0
MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learning—0
Simplicity within biological complexity—0
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource AllocationCode2
Fusion of Minutia Cylinder Codes and Minutia Patch Embeddings for Latent Fingerprint Recognition—0
Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model—0
VN Network: Embedding Newly Emerging Entities with Virtual Neighbors—0
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