Mapping dark networks
A data transformation method to study clandestine organizations
Bibliographic Data
| ID | 6161616 |
|---|---|
| Authors | Luke M Gerdes (United States Military Academy, corresponding author) |
| Year | 2014 |
| Volume | 2 |
| Issue | 2 |
| Pages | 213-253 |
| Publication date | 2014-05-07 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Network Science (JOURNAL) |
| Journal identifiers | ISSN: 2050-1250 • E-ISSN: 2050-1242 |
| Publisher | Cambridge University Press (PUBLISHER • US) |
| DOI | 10.1017/nws.2014.8 |
| OpenAlex | W2049090645 |
| Language | EN |
| Citations received | 3 |
| References cited | 29 |
There is a growing consensus that dark networks require special methodological considerations, but little attention has been devoted to determining which data transformation processes are best suited to the study of dark networks. Standard approaches to the transformation of multi-modal data “fold” matrices, by binarizing the network and multiplying it against its transpose. Unfortunately, this process produces exaggerated results when applied to weighted networks, and consequently researchers often disregard information on tie-strength when transforming data. This paper evaluates previous attempts to overcome this limitation and assesses projection methods discovered by biologists and physicists, who have studied the problem of transforming weighted multi-modal networks within the specific context of these disciplines. However, the assumptions underlying these transformation processes limit their applicability to dark networks. This paper, therefore, offers the Median Additive Projection Process (MAPP), an approach to data transformation specifically designed for implementation in weighted, multi-modal dark networks. MAPP accounts for the ambiguity inherent to clandestine subject matter by treating relationship strength in a probabilistic fashion. Because agents' one-mode centrality rankings change significantly when different projection processes are applied to the same two-mode network, MAPP allows researchers to more accurately identify central actors in dark networks
Algorithm · Ambiguity · Centrality · Context (archaeology · Data mining · Data science · Geography · Modal · Process (computing · Projection (relational algebra · Transformation (genetics · Complex Network Analysis Techniques · Computer Science · Evolutionary Game Theory and Cooperation · Mathematics · Opinion Dynamics and Social Influence · Artificial Intelligence · Theoretical Computer Science
Social Networks and Health
Identifying sets of key players in a social network
Analysis of weighted networks
Deep South
Mathematical foundations of computer science, 1979
Disrupting Dark Networks
Understanding Terror Networks
The Sage Handbook of Social Network Analysis
Leaderless Jihad
Dark Networks as Problems
Centrality in social networks conceptual clarification
Node centrality in weighted networks
Sorting out centrality
The application of network analysis to criminal intelligence
Rethinking centrality
Centrality in affiliation networks
Do all the pieces matter? Assessing the reliability of law enforcement data sources for the network analysis of wire taps
Strategies for Combating Dark Networks
Trust and Rule (Cambridge Studies in Comparative Politics)
| Citations per year | 0.3 |
|---|---|
| Citation span | 2016 - 2018 (3) |
| Highly cited | No |