Unsupervised Learning for Identifying High Eigenvector Centrality Nodes: A Graph Neural Network Approach.
Appan RakaraddiMahardhika PratamaPublished in: CoRR (2021)
Keyphrases
- unsupervised learning
- neural network
- adjacency matrix
- betweenness centrality
- graph structure
- directed graph
- weighted graph
- graph representation
- undirected graph
- complex networks
- shortest path
- graph matching
- supervised learning
- social network analysis
- back propagation
- graph laplacian
- graph structures
- social networks
- graph model
- network analysis
- laplacian matrix
- artificial neural networks
- attributed graphs
- semi supervised
- graph theory
- small world
- network structure
- edge weights
- fully connected
- planar graphs
- connected graphs
- spectral clustering
- data clustering
- dominating set
- root node
- overlapping communities
- terrorist networks
- nodes of a graph
- graph connectivity
- centrality measures
- random graphs
- spanning tree
- directed acyclic graph
- bipartite graph
- link prediction
- neural network model
- covariance matrix
- random walk
- feature space
- machine learning