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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 111120 of 403 papers

TitleStatusHype
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information NetworksCode0
Domain-adversarial Network AlignmentCode0
DyCSC: Modeling the Evolutionary Process of Dynamic Networks Based on Cluster StructureCode0
BiasedWalk: Biased Sampling for Representation Learning on GraphsCode0
NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic NetworksCode0
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vecCode0
Dynamic Embedding on Textual Networks via a Gaussian ProcessCode0
Network Embedding: on Compression and LearningCode0
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
Collaborative Graph Neural Networks for Attributed Network EmbeddingCode0
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