WordNet: a lexical database for English
Communications of the ACM
Unexpectedness as a measure of interestingness in knowledge discovery
Decision Support Systems - Special issue on WITS '97
Automatic query expansion via lexical-semantic relationships
Journal of the American Society for Information Science and Technology
Journal of the American Society for Information Science and Technology
COPLINK: managing law enforcement data and knowledge
Communications of the ACM
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Journal of the American Society for Information Science and Technology
On Incorporating Subjective Interestingness Into the Mining Process
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Algorithms for estimating relative importance in networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatically detecting deceptive criminal identities
Communications of the ACM - Homeland security
Graph-based technologies for intelligence analysis
Communications of the ACM - Homeland security
ACM SIGKDD Explorations Newsletter
ACM SIGKDD Explorations Newsletter
Retrieval evaluation with incomplete information
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Journal of the American Society for Information Science and Technology - Intelligence and Security Informatics
Criminal network analysis and visualization
Communications of the ACM - 3d hard copy
Topological analysis of criminal activity networks in multiple jurisdictions
dg.o '05 Proceedings of the 2005 national conference on Digital government research
CrimeLink explorer: using domain knowledge to facilitate automated crime association analysis
ISI'03 Proceedings of the 1st NSF/NIJ conference on Intelligence and security informatics
Untangling criminal networks: a case study
ISI'03 Proceedings of the 1st NSF/NIJ conference on Intelligence and security informatics
Using importance flooding to identify interesting networks of criminal activity
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Journal of the American Society for Information Science and Technology
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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.