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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

Showing 110 of 403 papers

TitleStatusHype
Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement LearningCode2
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource AllocationCode2
Random Walk on Multiple NetworksCode1
Learning Semantic Relationship Among Instances for Image-Text MatchingCode1
ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement LearningCode1
Multiplex Heterogeneous Graph Convolutional NetworkCode1
Online Knowledge Distillation via Mutual Contrastive Learning for Visual RecognitionCode1
Unsupervised Network Embedding Beyond HomophilyCode1
Monkey Business: Reinforcement learning meets neighborhood search for Virtual Network EmbeddingCode1
Signed Bipartite Graph Neural NetworksCode1
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