The European Physical Journal Special Topics

, Volume 222, Issue 6, pp 1413–1439 | Cite as

The importance of centralities in dark network value chains

  • Noemi TothEmail author
  • László GulyásEmail author
  • Richard O. LegendiEmail author
  • Paul DuijnEmail author
  • Peter M. A. SlootEmail author
  • George KampisEmail author
Regular Article Applications in Different Domains


This paper introduces three novel centrality measures based on the nodes’ role in the operation of a joint task, i.e., their position in a criminal network value chain. For this, we consider networks where nodes have attributes describing their “capabilities” or “colors”, i.e., the possible roles they may play in a value chain. A value chain here is understood as a series of tasks to be performed in a specific order, each requiring a specific capability. The first centrality notion measures how many value chain instances a given node participates in. The other two assess the costs of replacing a node in the value chain in case the given node is no longer available to perform the task. The first of them considers the direct distance (shortest path length) between the node in question and its nearest replacement, while the second evaluates the actual replacement process, assuming that preceding and following nodes in the network should each be able to find and contact the replacement. In this report, we demonstrate the properties of the new centrality measures using a few toy examples and compare them to classic centralities, such as betweenness, closeness and degree centrality. We also apply the new measures to randomly colored empirical networks. We find that the newly introduced centralities differ sufficiently from the classic measures, pointing towards different aspects of the network. Our results also pinpoint the difference between having a replacement node in the network and being able to find one. This is the reason why “introduction distance” often has a noticeable correlation with betweenness. Our studies show that projecting value chains over networks may significantly alter the nodes’ perceived importance. These insights might have important implications for the way law enforcement or intelligence agencies look at the effectiveness of dark network disruption strategies over time.


European Physical Journal Special Topic Spearman Rank Correlation Centrality Measure Betweenness Centrality Color Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© EDP Sciences and Springer 2013

Authors and Affiliations

  1. 1.Lorand Eötvös UniversityBudapestHungary
  2. 2.Aitia International, Inc.BudapestHungary
  3. 3.PetaByte Research Ltd.BudapestHungary
  4. 4.Research and AnalysisPolice Unit the HagueHagueThe Netherlands
  5. 5.University of AmsterdamAmsterdamThe Netherlands
  6. 6.Research Institute ITMOSt. PetersburgRussian Federation
  7. 7.Complexity ProgramNanyang Technological UniversitySingaporeSingapore
  8. 8.DFKI (German Research Insitute for Artificial Intelligence)KaiserslauternGermany

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