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Archive of posts filed under the Network Theory category.

Paper Submitted: AIDS and Behavior

Theory, Measurement and Hard Times: Some issues for HIV/AIDS research
Friedman, Samuel R
Sandoval, Milagros
Mateu-Gelabert, Pedro
Rossi, Diana
Gwadz, Marya
Dombrowski, Kirk
Smyrnov, Pavlo
Vasylyeva, Tetyana
Pouget, Enrique R
Perlman, David

Economic and political instability and related “big events” are widespread throughout the globe. Although they sometimes lead to epidemic HIV outbreaks, sometimes they do not—and we do not understand why. Current behavioural theories do not adequately address these processes, and thus cannot provide optimal guidance for effective intervention. Based in part on a critique of our prior “pathways” model of big events, we suggest that Cultural-Historical Activity Theory (CHAT) may provide a useful framework for HIV research in this area. Using CHAT concepts, we also suggest a number of areas in which new measures should be developed to make such research possible.

Key Words: Big events, hard times, cultural historical activity theory, theory, HIV, measurement

Paper Submitted: World Journal of AIDS

Topological and Historical Considerations for Infectious Disease Transmission among Injecting Drug Users in Bushwick, Brooklyn (USA)

Kirk Dombrowski (corresponding author)
Ric Curtis
Samuel R. Friedman

Recent interest by physicists in social networks and disease transmission factors has prompted debate over the topology of degree distributions in sexual networks. Social network researchers have been critical of ‘scale-free’ Barabasi-Albert approaches, and largely rejected the preferential attachment, ‘rich-get-richer’ assumptions that underlie that model. Instead, research on sexual networks has pointed to the importance of homophily and local sexual norms in dictating degree distributions, and thus disease transmission thresholds. Injecting Drug User Network topologies may differ from the emerging models of sexual networks, however. Degree distribution analysis of a Brooklyn, NY, IDU network indicates a different topology than the spanning tree configurations discussed for sexual networks, instead featuring comparatively short cycles and high concurrency. Our findings suggest that IDU networks do in some ways conform to a “scale-free” topology, and thus may represent “reservoirs” of potential infection despite seemingly low transmission thresholds.

Keywords: Social Network Analysis, Injecting Drug Users, Scale-Free Networks

Grant Application submitted to Submitted to AusAID DEVELOPMENT RESEARCH AWARDS SCHEME

PIs: Anthony Marcus and Chitra Raghavan
Methodologist: Kirk Dombrowski
Partner Organization: Trickle Up India & Trickle Up New York
Sept 21, 2012
A Social Network Analysis among Ultra-Poor Women in Eastern India
This research will examine the impact of poverty alleviation graduation programs on the social networks, social capital, and socio-economic outcomes of women living in ultra-poverty with the goal of developing new understandings of the nature of ultra-poverty and pathways out of that poverty. It draws on an assessment of a CGAP/Ford Foundation supported graduation program for the ultra-poor implemented by Trickle Up in northeast India, and implements the social network assessment techniques developed by SNRG for research in Inuit communities in Labrador, Canada. The proposal involves the novel use of validated social network research methods for the purpose of assessment and enhancement of the efficacy, scalability and sustainability of livelihoods programming for women and their households living in ultra-poverty.

Paper Submitted: Journal of Artificial Societies and Social Simulation

Modeling Dynamic Risk Networks
We describe a general framework for stochastic actor-based modeling of real-world dynamic risk networks. The models capture heterogeneity in the types of individuals, their interconnecting risk relationships, and the pathogens flowing between them. Dynamism is supported through arrival and departure processes, continuous restructuring of risk relationships, and changes to the pathogen over its lifetime. Whenever possible, the system is regulated through constraints on the local agency of individual nodes, risk relationships, and pathogen flows, rather than by system-wide specifications. We illustrate the application of the framework by applying it to a case study of HIV prevalence in injecting drug user (IDU) networks in New York City.
Khan, Dombrowski, Saad

Paper Accepted: Journal of Substance Use and Misuse

A Re-examination of Connectivity Trends via Exponential Random Graph Modeling in Two IDU Risk Networks

Kirk Dombrowski
Bilal Khan
Katherine McLean
Ric Curtis
Travis Wendel
Evan Misshula
Samuel Friedman

Abstract: Patterns of risk in injecting drug user (IDU) networks—including co-use, equipment sharing, and sex in the context of drug use—have been a key focus of network approaches to HIV and other disease transmission histories. New network modeling techniques allow for a re-examination of these patterns with greater statistical accuracy and the comparative weighting of model elements. This paper describes the results of a re-examination of network data from the SFHR and P90 data sets using Exponential Random Graph Modeling (ERGM). ERGM allows researchers to produce “regression-like” models to describe connection tendencies that consider both personal attributes (such as age, gender, race/ethnicity, and injection history) and network structural factors (such as transitivity—the tendency of an individual to form a network tie with the partner of a current partner). Similar to ordinary statistical modeling, ERGM network models provide model coefficients that demonstrate the relative weighting of such factors as they contribute to the formation of the networks in question, along with error estimation statistics. Cross-network comparison of the relative importance of model coefficients is also possible. These findings contribute to a more clear understanding of the “logic” of network connectivity among IDU networks.

Keywords: Injector Networks, ERGM, HIV Transmission, Network Modeling

This project was supported by NIH/NIDA Challenge Grant 1RC1DA028476-01/02 awarded to the CUNY Research Foundation and John Jay College, CUNY. Initial funding for a pilot version of this project was provided by the NSF Office of Behavioral, Social, and Economic Sciences, Anthropology Program Grant BCS-0752680.