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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 1–10 of 403 papers

TitleStatusHype
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource AllocationCode2
Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement LearningCode2
Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic SpaceCode1
Representation Learning for Attributed Multiplex Heterogeneous NetworkCode1
Adversarial Training Methods for Network EmbeddingCode1
Adversarial Privacy Preserving Graph Embedding against Inference AttackCode1
Adversarial Deep Network Embedding for Cross-network Node ClassificationCode1
DANE: Domain Adaptive Network EmbeddingCode1
Adaptive Graph Auto-Encoder for General Data ClusteringCode1
An Influence-based Approach for Root Cause Alarm Discovery in Telecom NetworksCode1
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