Limitations of low dimensional graph embeddings.
C. SeshadhriPublished in: IEEE Data Eng. Bull. (2023)
Keyphrases
- low dimensional
- high dimensional
- manifold learning
- graph embedding
- dimensionality reduction
- laplacian matrix
- high dimensional data
- principal component analysis
- vector space
- euclidean space
- dimension reduction
- feature space
- data points
- graph structure
- graph theory
- input space
- graph model
- random walk
- graph partitioning
- structured data
- lower dimensional
- graph representation
- multidimensional scaling
- directed graph
- weighted graph
- directed acyclic graph
- graph matching
- adjacency matrix
- connected components
- nonlinear dimensionality reduction
- data sets
- low dimensional spaces
- graph theoretic
- undirected graph
- similarity search
- social networks
- neural network