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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 231–240 of 403 papers

TitleStatusHype
User-based Network Embedding for Collective Opinion Spammer Detection—0
Using Distributional Thesaurus Embedding for Co-hyponymy Detection—0
Vertex-Context Sampling for Weighted Network Embedding—0
Video Tracking Using Learned Hierarchical Features—0
VNE Strategy based on Chaotic Hybrid Flower Pollination Algorithm Considering Multi-criteria Decision Making—0
VN Network: Embedding Newly Emerging Entities with Virtual Neighbors—0
WalkingTime: Dynamic Graph Embedding Using Temporal-Topological Flows—0
weg2vec: Event embedding for temporal networks—0
ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions—0
Zoo Guide to Network Embedding—0
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