graphComponents: Find clusters, and return node characteristics.

In haimbar/edgefinder: Detect Edges in Sparse Co-expression Graphs

Description

Take an adjacency Matrix as input and find clusters. For each node, find the degree and clustering coefficient (CC). Then, calculate a centrality measure (type\*CC+1)\*deg. For type=0, it's just the degree. Note that setting type=1 we assign a higher value to nodes that not only have many neighbors, but the neighbors are highly interconnected. For example, suppose we have two components with k nodes, one has a star shape, and the other is a complete graph. With type=0 both graphs will get the same value, but with type=1 the complete graph will be picked by the algorithm first. Setting type to a negative value gives CC\*deg as the centrality measure.

Usage

graphComponents(A, minCtr = 5, type = 1)

Arguments

A	An adjacency Matrix(0/1).
minCtr	The minimum centrality value to be considered for a cluster center (default=5).
type	Determines how the centrality measure is computed.

Value

A data frame with the following columns

• labels Node label (e.g. gene names).

• degree Node degree.

• cc Node clustering coefficient.

• ctr Node centrality measure: (type\*CC+1)\*deg, or CC\*deg if type is negative.

• clustNo Cluster number.

• iscenter 1 for the node was chosen as the cluster's center, 0 otherwise.

• intEdges Number of edges from the node to nodes in the same cluster.

• extEdges Number of edges from the node to nodes NOT in the same cluster.

• distCenter Standardized Manhattan distance to the central node.

Examples

   data(SIM)
   Sres <- edgefinder(SIM, ttl = "hub network")
   SimComp <- graphComponents(Sres$AdjMat)
   head(SimComp)

haimbar/edgefinder documentation built on Dec. 20, 2021, 2:45 p.m.