mexican_drug_trafficking — Mexican drug trafficking organizations and municipalities (1990-2010)
- Description
Temporal bipartite network of the presence of drug trafficking organizations (DTOs) in the municipalities of Mexico, for each year from 1990 to 2010, from M. Coscia and V. Rios. The data were extracted automatically from web content (Google News articles and other online sources), by detecting, for each year, which DTOs were mentioned together with which municipalities a significant number of times. Nodes are the 2,457 Mexican municipalities and 10 DTOs (the nine largest organizations, plus "Otros", which groups all other DTOs), and an edge with year $t$ connects a DTO to a municipality if the organization was found to operate in the municipality in year $t$; the same pair may be connected by several edges, one for each year. Data before 2004 are less reliable than later data, according to the authors. Municipalities without any detected DTO activity are included as isolated nodes.
The network has the following properties:
- Nodes:
is_organization, whether the node is a DTO (otherwise it is a municipality);name, the name of the DTO, as in the source (e.g. "Sinaloa", "Zetas", "Golfo"), or the INEGI code of the municipality (as a five-digit string with leading zero, e.g. "01001");state, the INEGI code of the state of the municipality (-1 for DTOs). - Edges:
year, the year of the activity.1
- Nodes:
- Tags
- Social Offline Unweighted Metadata Temporal Multigraph
- Citation
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- M. Coscia, V. Rios, "Knowing where and how criminal organizations operate using web content." Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM), 1412-1421 (2012) 10.1145/2396761.2398446 Sci-Hub
- Upstream URL OK
- https://www.michelecoscia.com/?page_id=1032
- Networks
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Tip: hover over a column header for a description.
Column legend
- $\left<k\right>$
- Mean degree
- $\sigma_k$
- Standard deviation of the degrees
- $\lambda_h$
- Largest eigenvalue of the Hashimoto (non-backtracking) matrix
- $\tau$
- Random walk mixing time
- $r$
- Degree assortativity
- $c$
- Global clustering coefficient
- $\oslash$
- (Pseudo-) diameter
- $S$
- Relative size of the largest component
- NPs
- Node properties
- EPs
- Edge properties