Unsupervised Graph Attention Autoencoder for Attributed Networks using K-means Loss.
Abdelfateh BekkairaSlimane BellaouarSlimane Oulad-NaouiPublished in: CoRR (2023)
Keyphrases
- k means
- normalized cut
- unsupervised clustering
- random walk
- edge weights
- clustering algorithm
- fully connected
- graph theory
- agglomerative clustering
- small world
- dynamic networks
- social networks
- unsupervised learning
- graph structures
- complex networks
- supervised learning
- bipartite graph
- graph structure
- network analysis
- clustering method
- directed graph
- graph layout
- spectral clustering
- community discovery
- supervised classification
- directed edges
- graph partitioning
- graph representation
- semi supervised
- graph clustering
- neural network
- weighted graph
- average degree
- structural patterns
- data clustering
- degree distribution
- network structure
- overlapping communities
- restricted boltzmann machine
- citation networks
- graph model
- social graphs
- random graphs
- path length
- power law
- graphical representation
- cluster centers
- graph databases
- directed acyclic graph
- graph matching
- hierarchical clustering
- cluster analysis
- self organizing maps
- structured data
- shortest path
- image segmentation
- learning algorithm