On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
IEEE Intelligent Systems
Unsupervised Link Discovery in Multi-relational Data via Rarity Analysis
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
AutoPart: parameter-free graph partitioning and outlier detection
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
The case for anomalous link discovery
ACM SIGKDD Explorations Newsletter
Relevance search and anomaly detection in bipartite graphs
ACM SIGKDD Explorations Newsletter
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Proceedings of the 3rd international workshop on Link discovery
Detecting anomalies in cargo using graph properties
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Graph-based approaches to insider threat detection
Proceedings of the 5th Annual Workshop on Cyber Security and Information Intelligence Research: Cyber Security and Information Intelligence Challenges and Strategies
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ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
Traffic dispersion graph based anomaly detection
Proceedings of the Second Symposium on Information and Communication Technology
Anomaly, event, and fraud detection in large network datasets
Proceedings of the sixth ACM international conference on Web search and data mining
Mining irregularities in maritime container itineraries
Proceedings of the Joint EDBT/ICDT 2013 Workshops
A clique-based method for mining fuzzy graph patterns in anti-money laundering systems
Proceedings of the 6th International Conference on Security of Information and Networks
A spectral approach to detecting subtle anomalies in graphs
Journal of Intelligent Information Systems
Visual analysis of large-scale network anomalies
IBM Journal of Research and Development
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An important area of data mining is anomaly detection, particularly for fraud. However, little work has been done in terms of detecting anomalies in data that is represented as a graph. In this paper we present graph-based approaches to uncovering anomalies in domains where the anomalies consist of unexpected entity/relationship alterations that closely resemble non-anomalous behavior. We have developed three algorithms for the purpose of detecting anomalies in all three types of possible graph changes: label modifications, vertex/edge insertions and vertex/edge deletions. Each of our algorithms focuses on one of these anomalous types, using the minimum description length principle to first discover the normative pattern. Once the common pattern is known, each algorithm then uses a different approach to discover particular anomalous types. In this paper, we validate all three approaches using synthetic data, verifying that each of the algorithms on graphs and anomalies of varying sizes, are able to detect the anomalies with very high detection rates and minimal false positives. We then further validate the algorithms using real-world cargo data and actual fraud scenarios injected into the data set with 100% accuracy and no false positives. Each of these algorithms demonstrates the usefulness of examining a graph-based representation of data for the purposes of detecting fraud.