Abstract
Typical terrorist cells/networks have dynamical structure as they evolve or adapt to changes which may occur due to capturing or killing of a member of the cell/network. Analytical measures in graph theory like degree centrality, betweenness and closeness centralities are very common and have long history of
their successful use in revealing the importance of various members of the network. However, modeling of covert, terrorist or criminal networks through social graph dose not really provide the hierarchical structure which exist in these networks as these networks are composed of leaders and followers etc. In this research we analyze and predict the most likely role a particular node can adapt once a member of the network is either killed or caught. The adaptation is based on computing Bayes posteriori probability of each node and the level of the said node in the network structure.
their successful use in revealing the importance of various members of the network. However, modeling of covert, terrorist or criminal networks through social graph dose not really provide the hierarchical structure which exist in these networks as these networks are composed of leaders and followers etc. In this research we analyze and predict the most likely role a particular node can adapt once a member of the network is either killed or caught. The adaptation is based on computing Bayes posteriori probability of each node and the level of the said node in the network structure.
| Original language | English |
|---|---|
| Title of host publication | Advanced Techniques in Computing Sciences and Software Engineering |
| Editors | Khaled Elleithy |
| Place of Publication | USA |
| Publisher | Springer Science+Business Media |
| Publication date | 2010 |
| Pages | 557-562 |
| Chapter | 95 |
| ISBN (Print) | 978-90-481-3659-9 |
| ISBN (Electronic) | 978-90-481-3660-5 |
| DOIs | |
| Publication status | Published - 2010 |