Link analysis

The third generation of link-analysis tools like DataWalk allow the automatic visualization of linkages between elements in a data set, that can then serve as the canvas for further exploration or manual updates.

Unsupervised learning methods can provide detection of broader issues, however, may result in a higher false-positive ratio if the behavioral norm is not well established or understood.

Data may contain "errors of omission and commission because of faulty collection or handling, and when entities are actively attempting to deceive and/or conceal their actions".

[17] Uncertainty in meaning of terms presents problems when attempting to search and cross reference data from multiple sources.

Link analysis techniques have primarily been used for prosecution, as it is far easier to review historical data for patterns than it is to attempt to predict future actions.

Alternatively, Picarelli argued that use of link analysis techniques could have been used to identify and potentially prevent illicit activities within the Aum Shinrikyo network.

"[3] Balancing the legal concepts of probable cause, right to privacy and freedom of association become challenging when reviewing potentially sensitive data with the objective to prevent crime or illegal activity that has not yet occurred.

There are four categories of proposed link analysis solutions:[21] Heuristic-based tools utilize decision rules that are distilled from expert knowledge using structured data.

Template-based tools employ Natural Language Processing (NLP) to extract details from unstructured data that are matched to pre-defined templates.

J.J. Xu and H. Chen propose a framework for automated network analysis and visualization called CrimeNet Explorer.