The attribute values shown bellow have been calcuated according to the full network.
Connections:
Organ systems
Endocrine
Cancer
Neurological
Pulmonary
Psychiatric
Renal
Cardiovascular
Ophthalmic
Dermatological
Genetic
Hematological
Gastrointestinal
Surgical
Autoimmune
Genitourinary
Musculoskeletal
Ear, nose and throat
Immune Deficiency
Immune
Gynecologycal
Prevalence is the proportion of persons in a population who have a particular disease
or attribute at a specified point in time or over a specified period of time.
The degree or degree centrality is the number of links a node or the network has.
It is a measure of connectivity of the diseases.
Betweenness centrality is a measure of information control or how a disease is a
“bridge” between other diseases, and represents the number of shortest paths
(i.e., the minimum number of links that connect every pair of nodes) that pass
through a node. “Betweenness centrality differs from the other centrality
measures we have considered in being not principally a measure of how well-
connected a vertex is. Instead it measures how much a node falls “between”
others. Indeed, a vertex can have quite low degree, be connected to others that
have low degree, even be a long way from others on average, and still have high
betweenness” (Newman M. (2010). Networks: An Introduction. Oxford
University Press).
The modularity of the network is the fraction of the edges that fall within the
given groups of nodes minus the expected fraction if edges were distributed at
random. In case that the number of links within groups exceeds the number
expected at random, modularity is considered positive. This measurement was
used to search for clusters of diseases within each network according to the
algorithm proposed by Blondel et al. (Blondel et al. (2008). Fast unfolding
communities in large networks. Journal of Statistical Mechanics: Theory and
Experiment).
Authority centrality. “A node or vertex has high authority centrality if it is
pointed to by many hubs (nodes with high degree), i.e., by many other vertices
with high hub centrality” (Newman M. (2010). Networks: An Introduction.
Oxford University Press).
Eigenvector centrality, is a modification of degree centrality, and it is a measure
that defines the centrality of a node according to its neighbors’ relevance or
prestige. “Instead of awarding vertices just one point for each neighbor,
eigenvector centrality gives each vertex a score proportional to the sum of the
scores of its neighbors” (Newman M. (2010). Networks: An Introduction.
Oxford University Press).
Closeness centrality, it measures the “closeness” to the other nodes, i.e., the
mean distance from a vertex to other vertices (Newman M. (2010). Networks:
An Introduction. Oxford University Press).
Local clustering coefficient. “It measures the fraction of pairs of neighbors of
vertex that are themselves neighbors” (Newman M. (2010). Networks: An
Introduction. Oxford University Press).