About: A Method for Estimating Driving Factors of Illicit Trade Using Node Embeddings and Clustering

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	A Method for Estimating Driving Factors of Illicit Trade Using Node Embeddings and Clustering
Creator	Finn, Lisa González, Jorgeángel Moloney, Gary Simske, Steven Winterlich, Anthony
Source	PMC
abstract	The trade on illegal goods and services, also known as illicit trade, is expected to drain 4.2 trillion dollars from the world economy and put 5.4 million jobs at risk by 2022. These estimates reflect the importance of combating illicit trade, as it poses a danger to individuals and undermines governments. To do so, however, we have to first understand the factors that influence this type of trade. Therefore, we present in this article a method that uses node embeddings and clustering to compare a country based illicit supply network to other networks that represent other types of country relationships (e.g., free trade agreements, language). The results offer initial clues on the factors that might be driving the illicit trade between countries.
has issue date	2020-04-29(xsd:dateTime)
bibo:doi	10.1007/978-3-030-49076-8_22
has license	no-cc
sha1sum (hex)	25f4f7e49d5429477fbffda8769422da1815d5e7
schema:url	https://doi.org/10.1007/978-3-030-49076-8_22
resource representing a document's title	A Method for Estimating Driving Factors of Illicit Trade Using Node Embeddings and Clustering
has PubMed Central identifier	PMC7297586
schema:publication	Pattern Recognition
resource representing a document's body	covid:25f4f7e49d5429477fbffda8769422da1815d5e7#body_text
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