Abstract
The wide-spread usage of network and graph based approaches in modeling data has been approved to be effective for various applications. The network based framework becomes more powerful when it is expanded to benefit from the widely available techniques for data mining and machine learning which allow for effective knowledge discovery from the investigated domain. The underlying reason for the substantial efficacy in studying graphs, either directly (i.e., data is given in graph format, for example, the “phone-call” network in studying social evolutions) or indirectly (network is inferred from data by predefined method or scheme, such as co-occurrence network for studying genetic behaviors), is the fact that graph structures emphasize the intrinsic relationship between entities, i.e., nodes (or vertices) in the network (in this chapter, the terms network and graph are used interchangeably). For the indirect case information extraction techniques may be adapted to investigate open sources of data in order to derive the required network structure as reflected in the current available data. This is a tedious process but effective and could lead to more realistic and up-to-date information reflected in the network. The latter network will lead to better and close to real-time knowledge discovery in case online information extraction is affordable and provided. Estimating network structure has attracted the attention of other researchers involved in terrorist network analysis, e.g.[9].
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Dawoud, K. et al. (2013). Data Analysis Based Construction and Evolution of Terrorist and Criminal Networks. In: Subrahmanian, V. (eds) Handbook of Computational Approaches to Counterterrorism. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5311-6_15
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