SBM_net {CommKern}

Simulated functional and structural connectivity with nested hierarchical community structure

Description

A dataset containing multimodal network information simulated to emulate functional and structural brain connectivity data with a nested hierarchical community structure. This dataset is a list containing five components in a format used as an input to the hms function. The components, and their associated variables, are as follows:

Usage

SBM_net

Format

A list containing five components:

func_edges

a dataframe containing 1233 rows and 3 columns: func_start_node, func_end_node, and func_weight. This dataframe describes the pairwise functional edge weights between nodes.

str_edges

a dataframe containing 453 rows and 3 columns: str_start_node, str_end_node, and str_weight. This dataframe describes the pairwise structural edge weights between nodes. There are fewer rows to this dataframe than func_edges as structural connectivity tends to be sparser than functional connectivity.

vertexes

a dataframe containing 80 rows and 5 columns: node_id, node_label, func_degree, str_degree, and community. The degree of a node is the sum of all edge weights connected to the node. In this simulated network, node_label is left as NA but, for other networks, a specific label may be used to denote additional information about the node. The community variable is left blank but is used by the hms algorithm.

func_matrix

an 80 x 80 matrix in the style of a network adjacency matrix. It contains the same information as func_edges, just in a wide, rather than long, format.

str_matrix

an 80 x 80 matrix in the style of a network adjacency matrix. It contains the same information as str_edges, just in a wide, rather than long, format.