Name
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. © 2008 Wiley Periodicals, Inc. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1002/asi.v59:13 | Journal of the Association for Information Science and Technology |
Keywords | Field | DocType |
criminal-network analysis,node importance heuristics,user-provided heuristics,link chart,interesting network,isi-related analysis tool,criminal activity,available data,importance flooding,privacy-sensitive data element,isi domain,network-based analysis,spreading activation,criminal justice | Data mining,Data modeling,Computer science,Computer security,Data sharing,Expert system,Association rule learning,Heuristics,Knowledge base,Law enforcement,Criminal justice | Journal |
Volume | Issue | ISSN |
59 | 13 | 1532-2882 |
ISBN | Citations | PageRank |
3-540-34478-0 | 11 | 0.56 |
References | Authors | |
29 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Byron Marshall | 1 | 275 | 21.46 |
| Hsinchun Chen | 2 | 9569 | 813.33 |
| Siddharth Kaza | 3 | 75 | 8.35 |