document: SNARE : a link analytic system for graph labeling and risk detection Classifying nodes in networks is a task with a wide range of applications . It can be particularly useful in anomaly and fraud detection . Many resources are invested in the task of fraud detection due to the high cost of fraud , and being able to automatically detect potential fraud quickly and precisely allows human investigators to work more efficiently . Many data analytic schemes have been put into use ; however , schemes that bolster link analysis prove promising . This work builds upon the belief propagation algorithm for use in detecting collusion and other fraud schemes . We propose an algorithm called SNARE ( Social Network Analysis for Risk Evaluation ) . By allowing one to use domain knowledge as well as link knowledge , the method was very successful for pinpointing misstated accounts in our sample of general ledger data , with a significant improvement over the default heuristic in true positive rates , and a lift factor of up to 6.5 ( more than twice that of the default heuristic ) . We also apply SNARE to the task of graph labeling in general on publicly-available datasets . We show that with only some information about the nodes themselves in a network , we get surprisingly high accuracy of labels . Not only is SNARE applicable in a wide variety of domains , but it is also robust to the choice of parameters and highly scalable-linearly with the number of edges in a graph .
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