centrality.measures

The centrality measures of a co-expression network of lncRNAs and
mRNAs in lung adenocarcinoma

datasets

Contains functions for the classification and ranking of top candidate features, reconstruction of networks from
adjacency matrices and data frames, analysis of the topology of the network
and calculation of centrality measures, and identification of the most
influential nodes. Also, a function is provided for running SIRIR model, which
is the combination of leave-one-out cross validation technique and the conventional SIR model, on a network to unsupervisedly rank the true influence of vertices. Additionally, some functions have been provided for the assessment
of dependence and correlation of two network centrality measures as well as
the conditional probability of deviation from their corresponding means in opposite direction.
Fred Viole and David Nawrocki (2013, ISBN:1490523995).
Csardi G, Nepusz T (2006). "The igraph software package for complex network research." InterJournal, Complex Systems, 1695.
Adopted algorithms and sources are referenced in function document.

Adrian Salavaty

influential

Identification and Classification of the Most Influential Nodes

centrality.measures function

A data frame with 794 rows and 6 variables:<dl>
 \
 <dt>DC</dt>
<dd>Degree Centrality</dd> <dt>CR</dt>
<dd>ClusterRank</dd> <dt>NC</dt>
<dd>Neighborhood Connectivity</dd> <dt>LH_index</dt>
<dd>Local H-index</dd> <dt>BC</dt>
<dd>Betweenness Centrality</dd> <dt>CI</dt>
<dd>Collective Influence</dd> ...</dl>

Format

Centrality measures dataset — centrality.measures

A data frame with 794 rows and 6 variables:<dl>
 \
 <dt>DC</dt>
<dd>Degree Centrality</dd>

 <dt>CR</dt>
<dd>ClusterRank</dd>

 <dt>NC</dt>
<dd>Neighborhood Connectivity</dd>

 <dt>LH_index</dt>
<dd>Local H-index</dd>

 <dt>BC</dt>
<dd>Betweenness Centrality</dd>

 <dt>CI</dt>
<dd>Collective Influence</dd>

 ...

</dl>

centrality.measures: Centrality measures dataset

Description

Usage

Arguments

Format