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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 191–200 of 403 papers

TitleStatusHype
GAHNE: Graph-Aggregated Heterogeneous Network Embedding—0
Unifying Homophily and Heterophily Network Transformation via Motifs—0
Adversarial Attacks on Deep Graph Matching—0
Exploiting Node Content for Multiview Graph Convolutional Network and Adversarial RegularizationCode0
User-based Network Embedding for Collective Opinion Spammer Detection—0
Multi-View Dynamic Heterogeneous Information Network Embedding—0
Toward Edge-Centric Network Embeddings—0
Embedding Node Structural Role Identity into Hyperbolic Space—0
Identifying Transition States of Chemical Kinetic Systems using Network Embedding Techniques—0
Deep Kernel Supervised Hashing for Node Classification in Structural Networks—0
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