Network centrality for the identification of biomarkers in respondent-driven sampling datasets

Abstract

Networks science techniques are frequently used to provide meaningful insights into the populations underlying medical and social data. This paper examines SATHCAP, a dataset related to HIV and drug use in three US cities. In particular, we use network measures such as betweenness centrality, closeness centrality, and eigenvector centrality to find central, important nodes in a network derived from SATHCAP data. We evaluate the attributes of these important nodes and create an exceptionality score based on the number of nodes that share a particular attribute. This score, along with the underlying network itself, is used to reveal insight into the attributes of groups that can be effectively targeted to slow the spread of disease. Our research confirms a known connection between homelessness and HIV, as well as drug abuse and HIV, and shows support for the theory that individuals without easy access to transportation are more likely to be central to the spread of HIV in urban, high risk populations.

Inferred networks, machine learning, and health data

“…The SATHCAP data are robust and lend themselves to many types of analysis. For example, the data can be subjected to standard statistical analysis [ 12 , 13 ], the referral chains can be interpreted as complex networks [ 14 ], and the survey design itself can be used to learn more about RDS [ 15 ]. SATHCAP is used here because of previous successful application to a similar dataset [ 16 ].…”

Abnormal hubs in global network as neuroimaging biomarker in right temporal lobe epilepsy at rest

“…Degree centrality (DC) is a whole-brain connectivity index that describes the global features of a given node through the use of graph theory models to assess the functional connectivity between that node and nodes throughout the brain ( 18 ). DC-based analytical approaches have recently been used to successfully evaluate patients diagnosed with schizophrenia, depression, and mild cognitive impairment ( 19 – 21 ).…”