Package index • netkit

Package overview

netkit netkit-package: netkit: Network Analysis and Visualization Toolkit

Graph input and annotation

Getting data into a graph and metadata onto it. Every function in netkit accepts either an igraph object or a data.frame edge list.

assign_attributes(): Assign Vertex and Edge Attributes to an igraph Graph
netkit-weights: Edge weights in netkit

Topology

Global and node-level descriptions of a network’s structure.

summarize_graph_metrics(): Summarize Topological Properties of a Graph
node_metrics(): Compute a Table of Node-Level Centrality Metrics
plot_CCDF(): Plot Complementary Cumulative Degree Distribution (CCDF)
compare_networks(): Compare Two Networks

Statistical testing

Whether an observed value is surprising, judged against a matched random ensemble.

null_model(): Generate Null-Model Graphs for Significance Testing
metric_significance(): Score an Observed Graph Against a Null Ensemble
small_worldness(): Small-World Coefficients

Node classification

Picking out structurally distinctive vertices.

find_hubs(): Identify Hub Nodes in a Network
find_bottlenecks(): Identify and Bottleneck Nodes in a Network
calculate_roles(): Calculate Network Roles Based on Within-Module Z-Score and Participation Coefficient

Community structure

find_modules(): Detect and Visualize Network Modules (Communities)

Subnetwork extraction

Building an interpretable neighbourhood around a set of nodes.

extract_subnetwork(): Extract an Interpretable Subnetwork Around a Set of Nodes

Diffusion

Propagating a signal from seed nodes, with a permutation null, and the inverse problem of choosing seeds to reach a target set.

network_diffusion(): Perform Network Diffusion from Seed Nodes
network_diffusion_with_pvalues(): Perform Network Diffusion from Seed Nodes
prepare_diffusion(): Prepare Diffusion Matrix
greedy_seed_selection(): Greedy Seed Node Selection to Maximize Diffusion Toward Target Nodes

Robustness

robustness_analysis(): Network Robustness Analysis via Node Removal Simulation

Visualization

plot_Net() is the central renderer; the others delegate to it. These draw with base graphics rather than returning a ggplot object.

plot_Net(): Plot an igraph network with customizable node sizes and edge widths
highlight_nodes(): Highlight Nodes in a Network Plot
layout_horizontal_tree(): Horizontal Tree Layout for Graph Visualization

Printing

How netkit’s results display at the console. Nothing here needs to be called directly.

print(<netkit_result>) print(<netkit_null>) `[`(<netkit_null>) print(<netkit_kernel>): Printing netkit objects