LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
IEEE Intelligent Systems
Discovering cluster-based local outliers
Pattern Recognition Letters
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Frequent sub-graph mining on edge weighted graphs
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
OddBall: spotting anomalies in weighted graphs
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
SigSpot: mining significant anomalous regions from time-evolving networks (abstract only)
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Finding the most descriptive substructures in graphs with discrete and numeric labels
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
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This paper presents Yagada, an algorithm to search labelled graphs for anomalies using both structural data and numeric attributes. Yagada is explained using several security-related examples and validated with experiments on a physical Access Control database. Quantitative analysis shows that in the upper range of anomaly thresholds, Yagada detects twice as many anomalies as the best-performing numeric discretization algorithm. Qualitative evaluation shows that the detected anomalies are meaningful, representing a combination of structural irregularities and numerical outliers.