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» Graph mining: Laws, generators, and algorithms
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PAKDD
2010
ACM
169views Data Mining» more  PAKDD 2010»
14 years 11 days 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
IJMMS
2007
166views more  IJMMS 2007»
13 years 7 months ago
Visualization of large networks with min-cut plots, A-plots and R-MAT
What does a ‘normal’ computer (or social) network look like? How can we spot ‘abnormal’ sub-networks in the Internet, or web graph? The answer to such questions is vital f...
Deepayan Chakrabarti, Christos Faloutsos, Yiping Z...
IADIS
2008
13 years 9 months ago
Data Mining In Non-Stationary Multidimensional Time Series Using A Rule Similarity Measure
Time series analysis is a wide area of knowledge that studies processes in their evolution. The classical research in the area tends to find global laws underlying the behaviour o...
Nikolay V. Filipenkov
WAW
2010
Springer
312views Algorithms» more  WAW 2010»
13 years 5 months ago
The Geometric Protean Model for On-Line Social Networks
We introduce a new geometric, rank-based model for the link structure of on-line social networks (OSNs). In the geo-protean (GEO-P) model for OSNs nodes are identified with points ...
Anthony Bonato, Jeannette Janssen, Pawel Pralat
ICDM
2002
IEEE
178views Data Mining» more  ICDM 2002»
14 years 16 days ago
gSpan: Graph-Based Substructure Pattern Mining
We investigate new approaches for frequent graph-based pattern mining in graph datasets and propose a novel algorithm called gSpan (graph-based Substructure pattern mining), which...
Xifeng Yan, Jiawei Han