Using importance flooding to identify interesting networks of criminal activity

Abstract

Effectively harnessing available data to support homeland‐security‐related applications is a major focus in the emerging science of intelligence and security informatics (ISI). Many studies have focused on criminal‐network analysis as a major challenge within the ISI domain. Though various methodologies have been proposed, none have been tested for usefulness in creating link charts. This study compares manually created link charts to suggestions made by the proposed importance‐flooding algorithm. Mirroring manual investigational processes, our iterative computation employs association‐strength metrics, incorporates path‐based node importance heuristics, allows for case‐specific notions of importance, and adjusts based on the accuracy of previous suggestions. Interesting items are identified by leveraging both node attributes and network structure in a single computation. Our data set was systematically constructed from heterogeneous sources and omits many privacy‐sensitive data elements such as case narratives and phone numbers. The flooding algorithm improved on both manual and link‐weight‐only computations, and our results suggest that the approach is robust across different interpretations of the user‐provided heuristics. This study demonstrates an interesting methodology for including user‐provided heuristics in network‐based analysis, and can help guide the development of ISI‐related analysis tools.

Can intermediary‐based science standards crosswalking work? Some evidence from mining the standard alignment tool (SAT)

“…Of course, a rich literature and tradition on multi‐attribute and information gain decision making, covering such diverse areas as consumer behavior and public policy making (e.g., Arentze & Timmermans, ; Thill, ; Timmermans et al., ), attest to this view of information retrieval. The value of multifaceted networks in facilitating retrieval processes has been demonstrated in law enforcement (Marshall, Chen, & Kaza, ), document summarization (Cai & Li, ), and database schema matching (Melnik et al., ), to name a few topical domains. However, for such techniques to be employed, relevance clues must be collected and organized for integration.…”

“…Educational resources being considered “near” previously vetted items or being positioned interestingly in a network of linked resources can provide these clues (White, ; Xu & Chen, ). Further, being positioned in or near interesting nodes in a network can provide usable relevance information (Lin & Chalupsky, ; Marshall et al., ). We offer that an approach that integrates multiple relevance clues, differential weighting of network neighborhood, as well as real‐time user input may well be needed to increase the effectiveness of standards‐based document retrieval systems.…”

“…The importance flooding algorithm discussed by Marshall et al. () is one such approach. In this and similar network propagation algorithms, relevance is transitively propagated across links and accumulates on “interesting” nodes.…”