@book{
 title = {Criminal Network Analysis and Mining (CRIMENET 2014) - Introduction},
 type = {book},
 year = {2015},
 source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
 identifiers = {[object Object]},
 volume = {8852},
 id = {7095cbd4-5f07-35a9-95a0-f0b62753e72d},
 created = {2017-01-10T18:22:00.000Z},
 file_attached = {false},
 profile_id = {d440742c-5825-34d8-8431-165bd244e675},
 last_modified = {2017-01-10T18:22:00.000Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 abstract = {© Springer International Publishing Switzerland 2015.Mobile phone networks, social network platforms, social media and over-IP messaging systems represent typical examples of the multitude of communication media broadly adopted in nowadays society. One aspect that has vast societal impact is the abuse of such platforms: the possibility that criminals can exploit these communication channels to organize and coordinate their illicit activities has been proved real. Criminal Networks (CNs) differ from well-studied Social Networks in a number of ways, including their size (usually the number of members is low), the lack of knowledge of their structure and organization (information about members and their relations is incomplete) and the different types of dynamics of interactions (digital communications, economic transactions, faceto- face interactions, etc.). Therefore, in recent years (say, after 9-11-2001) Criminal Network Analysis has grown as an outstanding, almost independent research area. The ability to detect criminal behavior across different interaction media is of paramount importance to avoid abuse and fight crime. For this reason, computational tools and models have been recently proposed to study criminal behavior in online platforms and mobile phone networks.},
 bibtype = {book},
 author = {Ferrara, E. and Catanese, S. and Fiumara, G.}
}

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