Analyzing the Terrorist Social Networks with Visualization Tools

Yang, Christopher C.; Liu, Nan; Sageman, Marc

doi:10.1007/11760146_29

Cited by 38 publications

(16 citation statements)

References 12 publications

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“…Using visualization tools, C Yang et al [69] have explored the complex network of Terrorists to determine the network structure. They have proposed a Spring Embedded algorithm which displays the Terrorist network by optimizing the usage of 2D space.…”

Section: Clusteringmentioning

confidence: 99%

An Overview on User Profiling in Online Social Networks

Vasanthakumar¹,

Sunithamma²,

Shenoy³

et al. 2017

IJAIS

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Advances in Online Social Networks is creating huge data day in and out providing lot of opportunities to its users to express their interest and opinion. Due to the popularity and exposure of social networks, many intruders are using this platform for illegal purposes. Identifying such users is challenging and requires digging huge knowledge out of the data being flown in the social media. This work gives an insight to profile users in online social networks. User Profiles are established based on the behavioral patterns, correlations and activities of the user analyzed from the aggregated data using techniques like clustering, behavioral analysis, content analysis and face detection. Depending on application and purpose, the mechanism used in profiling users varies. Further study on other mechanisms used in profiling users is under the scope of future endeavors.

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Section: Clusteringmentioning

confidence: 99%

An Overview on User Profiling in Online Social Networks

Vasanthakumar¹,

Sunithamma²,

Shenoy³

et al. 2017

IJAIS

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“…These difficulties hinder previous methods. In fact, in most of the recent studies on terrorist network analysis, the terrorist network was usually viewed a statical social network created at a certain time point, without considering its growth and evolution along time axis [11,12] . By doing so, the network is analyzed only once so that some important knowledge, such as "how did the size and structure of a network evolve", is not able to be fully discovered and then clearly visualized.…”

Section: Social Network Theorymentioning

confidence: 99%

Networked Data Mining Based on Social Network Visualizations

Yang¹,

Zhang²

2008

Journal of Software

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“…Unfortunately, the social network owned by individual agency may contain sensitive information. Such sensitive information is usually confidential due [19] to the privacy issue that causes the limitation on the degree of information sharing. Ideally, it is desired to share the crucial information in order to mine as much knowledge as if we have the complete information without violating the privacy.…”

Section: Introductionmentioning

confidence: 99%

“…Terrorist or criminal social networks have been proven to be useful for investigation, identifying suspects and gateways, and extracting communication patterns of terrorist or criminal organizations [5,19,20,21]. An illustration of a terrorist social network as presented in [19] is shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Information Integration for Terrorist or Criminal Social Networks

Yang

Tang

2009

Annals of Information Systems

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Social network analysis discovers knowledge embedded in the structure of social networks, which is useful for intelligence and law enforcement force in investigation. However, individual agency usually has part of the complete terrorist or criminal social network and therefore some crucial knowledge could not be extracted. Sharing information between different agencies will make such a social network analysis more effective, unfortunately the concern of privacy preservation usually prohibits the sharing of sensitive information. There is always a trade-off between the degree of privacy and the degree of utility in information sharing. Several approaches have been proposed to resolve such dilemma in sharing data from different relational tables. However, there is only limited amount of work on sharing social networks from different sources and yet trying to minimize the reduction on the degree of privacy. The work on privacy preservation of social network data relies on anonymity and perturbation. These techniques are developed for the purpose of data publishing, but ignore the utility of the published data on social network analysis and the integration of social networks from multiple sources. In this chapter, we propose a sub-graph generalization approach for information sharing and privacy preservation of terrorist or criminal social networks. The objectives are sharing the insensitive and generalized information to support social network analysis but preserving the privacy at the same time. Our experiment shows that such an approach is promising.

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Analyzing the Terrorist Social Networks with Visualization Tools

Cited by 38 publications

References 12 publications

An Overview on User Profiling in Online Social Networks

An Overview on User Profiling in Online Social Networks

Networked Data Mining Based on Social Network Visualizations

Information Integration for Terrorist or Criminal Social Networks

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