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Networks model of the East Turkistan terrorism
Abstract
The presence of the East Turkistan terrorist network in China can be traced back to the rebellions on the BAREN region in Xinjiang in April 1990. This article intends to research the East Turkistan networks in China and offer a panoramic view. The events, terrorists and their relationship are described using matrices. Then social network analysis is adopted to reveal the network type and the network structure characteristics. We also find the crucial terrorist leader. Ultimately, some results show that the East Turkistan network has big hub nodes and small shortest path, and that the network follows a pattern of small world network with hierarchical structure.
... If node i is linked to node j , then a i j = 1 , else a i j = 0 . The matrix also could have a weight between 0 and 1 to show the level of relation ( Li, Zhu, & Wang, 2015 ). The network is defined as a directed graph because terrorist leaders are learning tactics from mistakes and successes of past attacks, as seen in Fig. 8 . ...
... Degree Centrality: Degree centrality is calculated to draw topology of terrorist attacks and features, as seen in Eq. (8) ( Freeman, 1979;Li et al., 2015 ). This index helps to identify the most popular attacks in the network ( Sayama, 2015 ). ...
... Therefore, the central node has the smaller distance from others. This centrality measures the most efficient attack to collect information from the all terrorist groups ( Alvarez-Hamelin, Dall'Asta, Barrat, & Vespignani, 2005;Li et al., 2015;Sayama, 2015 ). where y ( j, h ) is the length of shortest path that connects nodes j and h . ...
... The relevance of this endeavor has motivated an increasing shift in modern forensic studies away from classical descriptive analyses of criminal networks (Krebs, 2002;Carley et al., 2002;Morselli, 2009;Malm and Bichler, 2011;Li et al., 2015;Agreste et al., 2016;Grassi et al., 2019;Cavallaro et al., 2020), and towards studying more complex group structures involving the monitored suspects (Ferrara et al., 2014;Calderoni and Piccardi, 2014;Magalingam et al., 2015;Calderoni et al., 2017;Robinson and Scogings, 2018;Liu et al., 2018;Sangkaran et al., 2020). These contributions have provided valuable initial insights into the internal structure and functioning of several criminal organizations. ...
Reliably learning group structure among the nodes in network data is challenging in several modern applications. We are particularly motivated by studying covert networks that encode relationships among criminals. These data are subject to measurement errors and often exhibit a complex combination of an unknown number of core-periphery, assortative and disassortative structures that may unveil the internal architecture of the criminal organization. The coexistence of such noisy block structures limits the reliability of community detection algorithms routinely applied to criminal networks, and requires extensions of model-based solutions to realistically characterize the node partition process, incorporate structured information from node attributes, and provide improved strategies for estimation, uncertainty quantification, model selection and prediction. To address these goals, we develop a novel class of extended stochastic block models (ESBM) that infer groups of nodes having common connectivity patterns via Gibbs-type priors on the partition process. This choice encompasses several realistic priors for criminal networks, covering solutions with fixed, random and infinite number of possible groups, and facilitates the inclusion of external node attributes in a principled manner. Among the new alternatives in our class, we focus on the Gnedin process as a realistic prior that allows the number of groups to be finite, random and subject to a reinforcement process coherent with the modular structures in organized crime. A general collapsed Gibbs sampler is proposed for the whole ESBM class, and refined strategies for estimation, prediction, uncertainty quantification and model selection are outlined. The ESBM performance is illustrated in realistic simulations and in an application to an Italian Mafia network, where we learn key block patterns revealing a complex hierarchical structure of the organization, mostly hidden from state-of-the-art alternative solutions.
... Approaches that rely on dynamic, multi-mode, multi-partite and meta-networks have been fruitful in shedding light on a wide variety of phenomena, including social ones [3,16,18,56]. In the last years, this process has indeed also touched areas as criminology, international security, and terrorism research [6,22,39] . ...
Given the extreme heterogeneity of actors and groups participating in terrorist actions, investigating and assessing their characteristics can be important to extract relevant information and enhance the knowledge on their behaviors. The present work will seek to achieve this goal via a complex networks approach. This approach will allow finding latent clusters of similar terror groups using information on their operational characteristics. Specifically, using open access data of terrorist attacks occurred worldwide from 1997 to 2016, we build a multi-partite network that includes terrorist groups and related information on tactics, weapons, targets, active regions. We propose a novel algorithm for cluster formation that expands our earlier work that solely used Gower's coefficient of similarity via the application of Von Neumann entropy for mode-weighting. This novel approach is compared with our previous Gower-based method and a heuristic clustering technique that only focuses on groups' ideologies. The comparative analysis demonstrates that the entropy-based approach tends to reliably reflect the structure of the data that naturally emerges from the baseline Gower-based method. Additionally, it provides interesting results in terms of behavioral and ideological characteristics of terrorist groups. We furthermore show that the ideology-based procedure tends to distort or hide existing patterns. Among the main statistical results, our work reveals that groups belonging to opposite ideologies can share very common behaviors and that Islamist/jihadist groups hold peculiar behavioral characteristics with respect to the others. Limitations and potential work directions are also discussed, introducing the idea of a dynamic entropy-based framework.
... This method extracts attacker interaction from network properties to obtain a better understanding of the attacker activity. The results could potentially help in the understanding of future attacks and enable counter-terrorism agencies to propose proactive strategies [11,13]. ...
Terrorist groups (attackers) always strive to outmaneuver counter-terrorism agencies with different tactics and strategies for making successful attacks. Therefore, understanding unexpected attacks (outliers) is becoming more and more important. Studying such attacks will help identify the strategies from past events that will be most dangerous when counter-terrorism agencies are not ready for protection interventions. In this paper, we propose a new approach that defines terrorism outliers in the current location by using non-similarities among attacks to identify unexpected interactions. The approach is used to determine possible outliers in future attacks by analyzing the relationships among past events. In this approach, we calculate the relationship between selected features based on a proposed similarity measure that uses both categorical and numerical features of terrorism activities. Therefore, extracting relations are used to build the terrorism network for finding outliers. Experimental results showed that the comparison of actual events and the detected patterns match with more than 90% accuracy for many future strategies. Based on the properties of the outliers, counter-terrorism agencies can prevent a future bombing attack on strategic locations.
... The accurate identification and analysis of the node roles in network are of great practical significance. For example, for a criminal network, it is hoped to more accurately identify all suspects, track and capture key members so as to effectively destroy the whole criminal network [2,3]. For social networks, it is hoped to analyze the role characteristics and behaviors of individuals so as to better plan the marketing, control the public opinions, and optimize the network services [4]. ...
Role analysis is a very important branch of complex network research, which tries to identify the node types with similar structures or functions. Inspired by the midfield in physics, and by extending and further expanding the concept of topological potential, a role identification and analysis method based on weighted directed topological potentials in connection with node “mass” (i.e. node own attribute) was introduced in this paper. This proposed method takes into consideration both the node own attributes and the network topological attributes, and identifies node roles by the difference in node topological potential. Its effectiveness was well verified by the experimental results on an IP communication network.
... Therefore, it is hard to understand terrorist activity through network analysis alone [5]. Tutun, Khanmohammadi, Chou and Kucuk Most of the current research uses network models in isolation without considering underlying predictive patterns in intelligence data sets [2,[6][7][8][9]. Using network analysis in isolation ignores the functional roles of individuals, though it is essential for terrorist activity detection because it captures interactions and gives a general idea of systems [5, 10]. ...
... Role analysis can be applied to the study of crime networks, social networks, communication networksand other fields [1][2][3][4] . The method of the role analysis may include two aspects: One is the network structure-based analysis，which is based on the network structure equivalence and the nodes similarity index to identify the nodes with similar structure [5][6][7][8] .The other one is the node attribute-based analysis. ...
... Most of the current research uses network models in isolation without considering underlying predictive patterns in intelligence data sets [2,[6][7][8][9]. Using network analysis in isolation ignores the functional roles of individuals, though it is essential for terrorist activity detection because it captures interactions and gives a general idea of systems [5,10]. ...
In recent years, terrorist attacks around the world have begun to develop more complex strategies and tactics that are not easily recognizable. Furthermore, in uncertain situations, agencies need to know whether the perpetrator was a terrorist or someone motivated by other factors (e.g. criminal activity) so that they can develop appropriate strategies to capture the responsible organizations and people. In most research studies, terrorist activity detection focuses on either individual incidents, which do not take into account the dynamic interactions among them, or network analysis, which leaves aside the functional roles of individuals while capturing interactions and giving a general idea about networks. In this study, we propose a unified approach that applies pattern classification techniques to network topology and features of incidents. The detected patterns are used in conjunction with an evolutionary adaptive neural fuzzy inference system to detect future incidents of terrorism. Finally, the proposed approach was tested and validated using a real world case study that consists of incidents in Iraq. The experimental results show that our approach outperforms other traditional detection approaches. Policymakers can use the approach for timely understanding and
detection of terrorist activity thus enabling precautions to be taken against future attacks.
Given the extreme heterogeneity of actors and groups participating in terrorist actions, investigating and assessing their characteristics can be important to extract relevant information and enhance the knowledge on their behaviors. The present work will seek to achieve this goal via a complex networks approach. This approach will allow to find latent clusters of similar terror groups using information on their operational characteristics. Specifically, using open access data of terrorist attacks occurred worldwide from 1997 to 2016, we build a multi-partite network that includes terrorist groups and related information on tactics, weapons, targets, active regions. We propose
a novel algorithm for cluster formation that expands our earlier work that solely used Gower’s coefficient of similarity via the application of Von Neumann entropy for mode-weighting. This novel approach is compared with our previous Gower-based method and a heuristic clustering technique that only focuses on groups’ ideologies.
The comparative analysis demonstrates that the entropy-based approach tends to reliably reflect the structure of the data that naturally emerges from the baseline Gower-based method. Additionally, it provides interesting results in terms of behavioral
and ideological characteristics of terrorist groups. We furthermore show that the ideology-based procedure tend to distort or hide existing patterns. Among the main statistical results, our work reveals that groups belonging to opposite ideologies can share very common behaviors and that Islamist/jihadist groups hold peculiar behavioral characteristics with respect to the others. Limitations and potential work directions are also discussed, introducing the idea of a dynamic entropy-based framework.
This study is interested in understanding public opinion in Pakistan toward terror attacks. Specifically, this study explores (1) the general picture of attitudes in Pakistan toward terrorism and (2) which individuals are most likely to support terrorism in Pakistan. The study aims to give insights into how pervasive a support base exists for terrorism as a tactic in Pakistan and it seeks to isolate the individual-level traits that account for the variation we see among Pakistani Muslims regarding their level of acceptance of terrorism against Pakistani and Indian targets. The study finds that a large majority of Pakistanis oppose terrorism but terrorism directed at Indian targets is more tolerated than terrorism against Pakistani targets. The study also finds that those who are most supportive of Talibanization in Pakistan are the most supportive of terrorism.
We analytically describe the architecture of randomly damaged uncorrelated networks as a set of successively enclosed substructures — k-cores. The k-core is the largest subgraph where vertices have at least k interconnections. We find the structure of k-cores, their sizes, and their birth points — the bootstrap percolation thresholds. We show that in networks with a finite mean number z2 of the second-nearest neighbors, the emergence of a k-core is a hybrid phase transition. In contrast, if z2 diverges, the networks contain an infinite sequence of k-cores which are ultra-robust against random damage.
We use the k-core decomposition to develop algorithms for the analysis of large scale complex networks. This decomposition, based on a re- cursive pruning of the least connected vertices, allows to disentangle the hierarchical structure of networks by progressively focusing on their cen- tral cores. By using this strategy we develop a general visualization algo- rithm that can be used to compare the structural properties of various net- works and highlight their hierarchical structure. The low computational complexity of the algorithm,O(n + e), where n is the size of the net- work, ande is the number of edges, makes it suitable for the visualization of very large sparse networks. We show how the proposed visualization tool allows to find specific structural fingerprints of networks.
We analytically describe the architecture of randomly damaged uncorrelated networks as a set of successively enclosed substructures--k-cores. The k-core is the largest subgraph where vertices have at least k interconnections. We find the structure of k-cores, their sizes, and their birthpoints--the bootstrap percolation thresholds. We show that in networks with a finite mean number zeta2 of the second-nearest neighbors, the emergence of a k-core is a hybrid phase transition. In contrast, if zeta2 diverges, the networks contain an infinite sequence of k-cores which are ultrarobust against random damage.
This paper looks at the difficulty in mapping covert networks. Analyzing networks after an event is fairly easy for prosecution purposes. Mapping covert networks to prevent criminal activity is much more difficult. We examine the network surrounding the tragic events of September 11th, 2001. Through public data we are able to map a portion of the network centered around the 19 dead hijackers.
We use the k-core decomposition to visualize large scale complex networks in two dimensions. This decomposition, based on a recursive pruning of the least connected vertices, allows to disentangle the hierarchical structure of networks by progressively focusing on their central cores. By using this strategy we develop a general visualization algorithm that can be used to compare the structural properties of various networks and highlight their hierarchical structure. The low computational complexity of the algorithm, O(n+e), where 'n' is the size of the network, and 'e' is the number of edges, makes it suitable for the visualization of very large sparse networks. We apply the proposed visualization tool to several real and synthetic graphs, showing its utility in finding specific structural fingerprints of computer generated and real world networks.
Modern terrorism events are so complex and diverse that the time and place of terrorism burst can't be predicted. However the occurrence of terrorism events still obeys some statistical distributions. Some research results were introduced for modern terrorism events from the view of statistical physics. The contents include the empirical results of the casualty number, the empirical results of inter-event time and some models which can explain the burst mechanism of terrorism events to some extent.
To understand spatiotemporal evolution of terrorism organization network and reveal its information mechanism. In this paper, from the view of network science, statistical physics and multi-agent method are proposed to explore time-space evolution of terrorism organization network. The model of time-space evolution is presented to understand terrorism organization network. In the evolution process, dynamical (correction) changes of nodes and edges with time and space for terrorism organization network are considered by designing algorithm. It is found in numerical simulation that, the evolution of terrorism organization network possess small-world effect and scale-free property at the same time, and the whole network have hierarchical structure with different level centre nodes. Increasing factors of belief and resource can also effect on continuous evolution of network. If government around the world take effectively selected measures to attack the terrorism organization, which can be inhibited to a certain extent or under control. But the counter terrorism is the future still a long-term arduous.
Is there any kind of "memory" in the sequence of terror attacks worldwide? Are the terrorist attacks non-randomly time distributed? Our analysis suggests that they are correlated, which means that a terror event is not independent from the time elapsed since the previous event. But, if we consider terror attacks with a large severity index (number of fatalities or injured), the phenomenon is unpredictable, since it approaches a Poisson process (random, independent and uncorrelated). (c) 2005 Elsevier B.V. All rights reserved.
Lone wolf terrorism remains an ambiguous and enigmatic phenomenon. The boundaries of lone wolf terrorism are fuzzy and arbitrary. This article aims to define and analyze the main features and patterns of lone wolf terrorism in fifteen countries. Lone wolf terrorism is shown to be more prevalent in the United States than in the other countries under study. The cross-national analysis suggests that in the United States lone wolf terrorism has increased markedly during the past three decades; a similar increase does not appear to have occurred in the other countries under study. The numbers of casualties resulting from lone wolf terrorism have been relatively limited, and there is no evidence that the lethality of lone wolf terrorism is on the increase. The rates of psychological disturbance and social ineptitude are found to be relatively high among lone wolf terrorists. Lone wolf terrorists tend to create their own ideologies that combine personal frustrations and aversion with broader political, social, or religious aims. In this process, many lone wolf terrorists draw on the communities of belief and ideologies of validation generated and transmitted by extremist movements.
The September 11 attack on the US has revealed an unprecedented terrorism with worldwide range of destruction. It is argued to result from the first worldwide percolation of passive supporters. They are people sympathetic to the terrorism cause but without being involved with it. They just do not oppose it in case they could. This scheme puts suppression of the percolation as the major strategic issue in the fight against terrorism. Acting on the population is shown to be useless. Instead a new strategic scheme is suggested to increase the terrorism percolation threshold and in turn suppress the percolation. The relevant associated space is identified as a multi-dimensional social space including both the ground earth surface and all various independent flags displayed by the terrorist group. Some hints are given on how to shrink the geographical spreading of terrorism threat. The model apply to a large spectrum of clandestine activities including guerilla warfare as well as tax evasion, corruption, illegal gambling, illegal prostitution and black markets.
The intuitive background for measures of structural centrality in social networks is reviewed and existing measures are evaluated in terms of their consistency with intuitions and their interpretability.Three distinct intuitive conceptions of centrality are uncovered and existing measures are refined to embody these conceptions. Three measures are developed for each concept, one absolute and one relative measure of the centrality of positions in a network, and one reflecting the degree of centralization of the entire network. The implications of these measures for the experimental study of small groups is examined.
With the rapid development of Internet, cybercrime by means of Internet become serious. Mining their communication can be
used to discover some latent criminal activities. Social network analysis is used for understanding their social communication
status. But promulgating information on Internet is free. Criminals can hide in any corner by anonymity or forging their personal
information. Authorship identification methods based on stylometry is necessary to identify criminal’s real authorship. So
in this paper, social network analysis methods based on e-mail and blog was provided. Authorship identification using to judge
authorship’s authenticity was proposed. Experiments on e-mail and blog dataset’s social network analysis were demonstrated.
Knowledge about the structure and organization of criminal networks is important for both crime investigation and the development of effective strategies to prevent crimes. However, except for network visualization, criminal network analysis remains primarily a manual process. Existing tools do not provide advanced structural analysis techniques that allow extraction of network knowledge from large volumes of criminal-justice data. To help law enforcement and intelligence agencies discover criminal network knowledge efficiently and effectively, in this research we proposed a framework for automated network analysis and visualization. The framework included four stages: network creation, network partition, structural analysis, and network visualization. Based upon it, we have developed a system called CrimeNet Explorer that incorporates several advanced techniques: a concept space approach, hierarchical clustering, social network analysis methods, and multidimensional scaling. Results from controlled experiments involving student subjects demonstrated that our system could achieve higher clustering recall and precision than did untrained subjects when detecting subgroups from criminal networks. Moreover, subjects identified central members and interaction patterns between groups significantly faster with the help of structural analysis functionality than with only visualization functionality. No significant gain in effectiveness was present, however. Our domain experts also reported that they believed CrimeNet Explorer could be very useful in crime investigation.
Part I. Introduction: Networks, Relations, and Structure: 1. Relations and networks in the social and behavioral sciences 2. Social network data: collection and application Part II. Mathematical Representations of Social Networks: 3. Notation 4. Graphs and matrixes Part III. Structural and Locational Properties: 5. Centrality, prestige, and related actor and group measures 6. Structural balance, clusterability, and transitivity 7. Cohesive subgroups 8. Affiliations, co-memberships, and overlapping subgroups Part IV. Roles and Positions: 9. Structural equivalence 10. Blockmodels 11. Relational algebras 12. Network positions and roles Part V. Dyadic and Triadic Methods: 13. Dyads 14. Triads Part VI. Statistical Dyadic Interaction Models: 15. Statistical analysis of single relational networks 16. Stochastic blockmodels and goodness-of-fit indices Part VII. Epilogue: 17. Future directions.
NewsA few months ago, Lawrence Wilkerson, a former U.S. State Department official and Army colonel, painted a nightmare scenario of how social network science can be applied in a battle zone. Describing how U.S. forces gathered intelligence to identify networks of insurgents after the 2003 invasion of Iraq, Wilkerson outlined something he called "the mosaic philosophy." The strategy, he claims, was similar to sequencing a genome. But instead of assembling millions of strands of DNA, investigators worked with data from interrogations of thousands of civilian prisoners. The general strategy of casting a wide net for intelligence gathering was familiar to all network researchers contacted by Science (see [main text][1]), but many expressed disbelief that it was carried out on such a grand scale in Iraq and Afghanistan.
[1]: http://www.sciencemag.org/cgi/content/short/325/5939/409
NewsA decade ago, most research on social networks was abstract and academic. But in the wake of the 11 September 2001 attacks, interest in applying this research to warfare exploded. Many companies are now vying for a piece of the military funding. Academic network scientists are also diving in, competing for lucrative U.S. military contracts and grants. In spite of the boom, there is sharp disagreement about how effective social network analysis has been for counterterrorism. Some worry that in the rush to catch terrorists, the U.S. military has put too much faith in social network analysis. One former U.S. official even claims that applying these methods in war zones has led to unethical practices ([see sidebar][1]).
[1]: http://www.sciencemag.org/cgi/content/full/325/5939/410
A brief review of applications of social networks analysis against terrorism
- Ben
- Li
- Li
Ben-xian Li, Meng-Jun Li, et al., A brief review of applications of social networks analysis against terrorism, Complex Syst. Complex. Sci. 9 (2012) 84–94.
Origin and Development of the East Turksitan in Xinjiang, Xingjiang People's Press, Xinjiang
- Sheng
- Li
Sheng Li, Origin and Development of the East Turksitan in Xinjiang, Xingjiang People's Press, Xinjiang, 2009, pp. 1–30.