Graph eigenvectors, fundamental weights and centrality metrics for nodes in networks.
Piet Van MieghemPublished in: CoRR (2014)
Keyphrases
- betweenness centrality
- edge weights
- complex networks
- network structure
- weighted graph
- centrality measures
- shortest path
- social networks
- average degree
- social network analysis
- directed graph
- real world networks
- heterogeneous social networks
- small world
- graph structures
- network analysis
- bipartite graph
- real world social networks
- fully connected
- directed edges
- graph structure
- neighboring nodes
- clustering coefficient
- spanning tree
- eigenvalues and eigenvectors
- community structure
- overlapping communities
- graph partitioning
- adjacency matrix
- principal component analysis
- graph clustering
- graph laplacian
- random walk
- graph theory
- community detection
- path length
- network size
- undirected graph
- graph model
- terrorist networks
- real world graphs
- graph mining
- community detection algorithms
- densely connected
- sparsely connected
- laplacian matrix
- small world networks
- covariance matrix
- spectral clustering
- graph mining algorithms
- connected graphs
- scale free
- link prediction
- directed acyclic graph
- weight matrix
- community discovery
- graph representation
- biological networks
- random graphs