Covert networks datasets compiled by the Mitchell Centre for Social Network Analysis, University of Manchester and funded by a grant from the Leverhulme Trust (RPG-2013-140).
http://www.socialsciences.manchester.ac.uk/mitchell-centre/research/covert-networksMost datasets are available in both UCINET and Excel .csv formats.
A covert network is a social network which has one or many elements of secrecy about it.
Network members may try to keep their identities secret (as with criminal or terrorist organizations); the network may form around activities which have to be kept secret because they are illegal or dangerous (such as covert social movements like the Suffragettes), or for other reasons.
DESCRIPTION: This data set is about the evolution of a friendship network and delinquent behavior of pupils in school classes, collected in the Dutch Social Behavior study, a two-wave survey in classrooms (Houtzager and Baerveldt, 1999). These data are from classrooms of the MAVO track, the lower middle level of the Dutch secondary school system, in which the pupils filled in a questionnaire in the 3d and 4th years, with about one year in between.DATA FORMAT: .csv
DATA: The data files are for 19 schools, numbered h = 1, 3, 4, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23. For each of these values of h, the following files are available:
Network data files (adjacency matrices):
wave 1: N34_h.csv (grade 3)
wave 2: HN34_h.csv (grade 4)
The relation is defined as giving and receiving emotional support: there is a tie from pupil i to pupil j if i says that he/she receives and/or gives emotional support from/to pupil j.
with the variables, respectively:
the same measure of delinquent behavior, measured at waves 1 and 2, transformed by ln(1+x), but now also rounded to integer values.
defined as 1 if the pupils have the same ethnic background, and 0 otherwise.
SOURCE/AVAILABILITY: Freely downloadable from https://www.stats.ox.ac.uk/~snijders/siena/BaerveldtData.html
CITATIONS: Houtzager, B. & Baerveldt, C. (1999). Just like Normal. A Social Network Study of the Relation between Petty Crime and the Intimacy of Adolescent Friendships. Social Behavior and Personality 27(2), 177-192.
Snijders, Tom A.B, and Baerveldt, Chris (2003). A Multilevel Network Study of the Effects of Delinquent Behavior on Friendship Evolution. Journal of Mathematical Sociology 27, 123-151.
Chris Baerveldt, Beate Völker, and Ronan Van Rossem (2008). Revisiting selection and influence: an inquiry into the friendship networks of high school students and their association with delinquency. Canadian Journal of Criminology and Criminal Justice, 50, 559-587.
KEYWORDS: young people, Netherlands
DESCRIPTION: Network of hyperlinks between domestic terrorist group websites in the United States.DATA FORMATS: UCINET, .csv
DATA: 1-mode matrix 32 x 32 website by website
Directed binary ties are based on analysis of hyperlinks between sites.
SOURCE: Available from Manchester
CITATION: Zhou et al. (2005), ‘US domestic extremist groups on the web: link and content analysis’, available at http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1511999&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D1511999
KEYWORDS: Terrorist, United States
DESCRIPTION: Data is organisation-to-organisation links of terrorist organisations operating in the Indian State of Jammu & Kashmir.DATA FORMAT: UCINET, .csv
DATA: Four 1-mode matrices persons by persons for years 2000 (5 x 5), 2001 (25 x25), 2002 (23 x 23), 2003 (18 x 18).
Undirected, binary ties are "co-occurrence" mentions of terrorist organisations together in various sources e.g. on-line
SOURCE: Available from Manchester
CITATION: Sudhir Saxena, K. Santhanam, Aparna Basu (2004), 'Application of social network analysis (SNA) to terrorist networks in Jammu & Kashmir’, Strategic Analysis 28(1)
Available from Manchester.
KEYWORDS: Terrorist, India
DESCRIPTION: 2-mode dataset describing groups allied to Islamic State and the countries in which they are operatingDATA FORMAT: UCINET, .csv
DATA: 2-mode matrix 47 x 20 organizations by state, undirected binary ties.
SOURCE: Available from Manchester
DESCRIPTION: Data is on attendance of suspected members of the Ndrangheta criminal organization at summits (meetings whose purpose is to make important decisions and/or affiliations, but also to solve internal problems and to establish roles and powers) taking place between 2007 and 2009.DATA FORMAT: UCINET, .csv
DATA: 2-mode matrix 156 x 47 persons by events (summits), undirected binary ties.
Attendance at events have been registered by police authorities through wiretapping and observations during the large investigation called "Operazione Infinito".
SOURCE: The data has been reconstructed by the document "ORDINANZA DI APPLICAZIONE DI MISURA COERCITIVA con mandato di cattura - art. 292 c.p.p. -" which is available online at the following address http://www.stampoantimafioso.it/documentazione-antimafia/ordinanze/.
Stampo Antimafioso is a project which aims to share information about the Mafia operating in Northem Italy.
The dataset has been reconstructed by mostly referring to pp.87-110 of the document named "Operazione Infinito". This report is a judicial document concerning the pre-trial detention order triggered by the the preliminary investigation judge (Giudice per le indagini preliminari) of Milan. With this judicial act, measures of custody and pretrial detention have been ordered for the reported suspected of 'Ndrangheta affiliation.
KEYWORDS: Criminal, Italy
DESCRIPTION: Data is on co-offending in a London-based inner-city street gang, 2005-2009, operating from a social housing estate. Data comes from anonymised police arrest and conviction data for ‘all confirmed’ members of the gang.DATA FORMAT: UCINET, .csv
DATA: 1-Mode matrix 54 x 54 persons by persons, undirected, valued.
Network tie values:
= 1 (hang out together)
= 2 (co-offend together)
= 3 (co-offend together, serious crime)
= 4 (co-offend together, serious crime, kin)
Attributes: Age, Birthplace (1 = West Africa, 2= Caribbean, 3= UK, 4= East Africa), Residence, Arrests, Convictions, Prison, Music.
CITATION: Grund, T. and Densley, J. (2015) Ethnic Homophily and Triad Closure: Mapping Internal Gang Structure Using Exponential Random Graph Models. Journal of Contemporary Criminal Justice, Vol. 31, Issue 3, pp. 354-370
Grund, T. and Densley, J. (2012) Ethnic Heterogeneity in the Activity and Structure of a Black Street Gang. European Journal of Criminology, Vol. 9, Issue 3, pp. 388-406.
SOURCE: Available from Manchester.
KEYWORDS: Gangs, criminal, London, United Kingdom
DESCRIPTION: Data on couples attending swinging parties.
DATA FORMAT: UCINET, .csv
DATA: 2-mode matrix 57 x 39 couples by events (parties)
"Swing units" are a couple attending events with other "swing units".
SOURCE: Data from Anne-Marie Niekamp. Available from Manchester.
DESCRIPTION: The data comes from a Czech media database called Newton Media Search and involves all major Czech newspapers for the period from 4th June 2013 to 4th June 2014.DATA FORMATS: UCINET, .csv
Jana Nagyová, Petr Nečas – former prime minister and his office chief and love affair.
Ivan Fuksa, Petr Tluchoř, Marek Šnajdr – deputies of ODS (conservative governing party at that time)
Ondrej Páleník, Roman Boček, Jan Pohůnek, Milan Kovanda, Lubomír Poul, Libor Grygárek – high government officials and espionage agents
Ivo Rittig, Roman Janoušek, Václav Ryba, Tomáš Hrdlička, Jiří Toman – eminences gris, "godfathers"
DATA: 1-mode matrix 16 x 16 person by person.
The ties are co-appearances – every time an actor was mentioned in one article together with any other actor, it is considered to be a tie.
Ties are valued on am 11 point scale, where 10 is the strongest tie (Nagyova – Necas).
All other ties were transformed by dividing the total number of co-appearances between the two actors by the value of the strongest tie, which gave the percent of the maximal tie. This percentage was then assigned an integer value from range 0 - 9 according to which tenth of percents this particular value falls into.
Example: The Fuksa - Nagyova tie reaching 50% of the strongest tie value was assigned a value of 5. The Nagyova - Ryba tie reaching 3% of the max value was assigned zero etc.
SOURCE: Data from Tomas Diviák, available from Manchester.
KEYWORDS: Czech Republic, Czechia, political
DESCRIPTION: Data is on militant organizations between 1985 and 2006. Each node signifies a militant organization or other type of entity that conducts suicide attacks.DATA FORMAT: UCINET, .csv
DATA: 1-mode matrix for each year, organization by organization. Undirected, binary ties represent a known physical relationship between agents from different but “connected” organizations.
1985 10 x 10
1986 4 x 4
1990 4 x 4
1993 4 x 4
1994 4 x 4
1995 9 x 9
1996 6 x6
1997 4 x 4
1998 10 x 10
1999 7 x 7
2000 10 x 10
2001 15 x 15
2002 11 x 11
2003 21 x 21
2004 25 x 25
2005 27 x 27
2006 31 x 31
SOURCE: Available from Manchester. Reconstructed from Benjamin Acosta & Steven J. Childs (2013) ‘Illuminating the Global Suicide-Attack Network’, Studies in Conflict & Terrorism, 36:1, 49-76
CITATION: Benjamin Acosta & Steven J. Childs (2013) ‘Illuminating the Global Suicide-Attack Network’, Studies in Conflict & Terrorism, 36:1, 49-76
KEYWORDS: Terrorist, militant, political
DESCRIPTION: Data collected by the Center for Computational Analysis of Social and Organizational Systems, a research group at Carnegie Mellon University, on the participation of 18 Al Qaeda members in 25 functional tasks underlying the 1998 bombings of the U.S. Embassies in Nairobi, Kenya, and Dar es Salaam, Tanzania DATA: 2-Mode persons to Standing Committees.DATA FORMAT: UCINET, .csv
DATA: 2-mode matrix 18 x 25 persons to tasks, binary undirected. Relations are participation in tasks.
SOURCE: Available from Center for Computational Analysis of Social and Organizational Systems (CASOS). (2008). Tanzania-Kenya-imoon.xml. Data available online: http://www.casos.cs.cmu.edu/ computational_tools/datasets/internal/tanzania_ kenya/index11.php.
Also available from Manchester.
CITATION: Gerdes, Luke M. (2014), ‘Dependency Centrality from Bipartite Social Networks’, Connections, 34, 1&2
KEYWORDS: Terrorist, Al Qaeda, Kenya, Tanzania