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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 91–100 of 403 papers

TitleStatusHype
Representation Learning on Heterostructures via Heterogeneous Anonymous WalksCode0
DeHIN: A Decentralized Framework for Embedding Large-scale Heterogeneous Information Networks—0
Learning Large-scale Network Embedding from Representative Subgraph—0
WalkingTime: Dynamic Graph Embedding Using Temporal-Topological Flows—0
Network representation learning: A macro and micro view—0
High-order joint embedding for multi-level link prediction—0
MetaMIML: Meta Multi-Instance Multi-Label Learning—0
Community detection using low-dimensional network embedding algorithms—0
Barlow Graph Auto-Encoder for Unsupervised Network Embedding—0
TME-BNA: Temporal Motif-Preserving Network Embedding with Bicomponent Neighbor AggregationCode0
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