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Table 2 Graph theoretical features of functional connectivity networks

From: A probabilistic approach for pediatric epilepsy diagnosis using brain functional connectivity networks

Feature Feature description Feature calculation
ldg Link density of the graph (2 × ne)/(nn × (nn - 1))
acc Average of closeness centrality (1/nn) ∑ nn (sum of reciprocal distances from a node to all other nodes)
gcc Graph clustering coefficient (3 × number of triangles)/(number of connected triples of nodes)
rcc Rich club coefficient (ne_k)/(nn_k × (nn k - 1))
smg S-metric of graph Sum of the nodal degree products for every edge.
acg Algebraic connectivity of graph Second smallest eigenvalue of the Laplacian of adjacency matrix.
eng Energy of network graph Sum of absolute values of the real components of eigenvalues of adjacency matrix.
  1. ne: Number of graph edges; nn: number of graph nodes;
  2. nn_k: Number of nodes with degree larger thank; ne_k: number of edges among nodes with degree larger than k.