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ICDM
2005
IEEE
190views Data Mining» more  ICDM 2005»
13 years 10 months ago
Neighborhood Formation and Anomaly Detection in Bipartite Graphs
Many real applications can be modeled using bipartite graphs, such as users vs. files in a P2P system, traders vs. stocks in a financial trading system, conferences vs. authors ...
Jimeng Sun, Huiming Qu, Deepayan Chakrabarti, Chri...
ICDM
2007
IEEE
156views Data Mining» more  ICDM 2007»
13 years 8 months ago
Computing Correlation Anomaly Scores Using Stochastic Nearest Neighbors
This paper addresses the task of change analysis of correlated multi-sensor systems. The goal of change analysis is to compute the anomaly score of each sensor when we know that t...
Tsuyoshi Idé, Spiros Papadimitriou, Michail...
ISI
2007
Springer
13 years 10 months ago
Detecting Anomalies in Graphs
Graph data represents relationships, connections, or affinities. Innocent relationships produce repeated, and so common, substructures in graph data. We present techniques for dis...
David B. Skillicorn
PAKDD
2010
ACM
169views Data Mining» more  PAKDD 2010»
13 years 9 months ago
oddball: Spotting Anomalies in Weighted Graphs
Given a large, weighted graph, how can we find anomalies? Which rules should be violated, before we label a node as an anomaly? We propose the OddBall algorithm, to find such nod...
Leman Akoglu, Mary McGlohon, Christos Faloutsos
WISTP
2010
Springer
13 years 11 months ago
A Probabilistic Diffusion Scheme for Anomaly Detection on Smartphones
Widespread use and general purpose computing capabilities of next generation smartphones make them the next big targets of malicious software (malware) and security attacks. Given ...
Tansu Alpcan, Christian Bauckhage, Aubrey-Derrick ...