Graph Theory and Network Embedding

This cluster focuses on the theoretical foundations and methodologies surrounding graph theory, particularly in the context of embedding techniques that enable better representation and analysis of complex networks. It encompasses research on connectivity metrics, modern graph structures, and advanced neural network approaches to analyze heterogeneous graphs.

graph embeddings
connectivity
network structures
heterogeneous graphs
deep learning
graph metrics
topology
representation learning

26,104 papers

Parent topic: Graph Theory and Visualization

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Papers Over Time

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