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» MING: mining informative entity relationship subgraphs
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DILS
2006
Springer
14 years 6 days ago
Link Discovery in Graphs Derived from Biological Databases
Public biological databases contain vast amounts of rich data that can also be used to create and evaluate new biological hypothesis. We propose a method for link discovery in biol...
Petteri Sevon, Lauri Eronen, Petteri Hintsanen, Ki...
PAKDD
2009
ACM
134views Data Mining» more  PAKDD 2009»
14 years 5 months ago
On Link Privacy in Randomizing Social Networks.
Many applications of social networks require relationship anonymity due to the sensitive, stigmatizing, or confidential nature of relationship. Recent work showed that the simple ...
Xiaowei Ying, Xintao Wu
ICDM
2009
IEEE
117views Data Mining» more  ICDM 2009»
14 years 3 months ago
Clustering with Multiple Graphs
—In graph-based learning models, entities are often represented as vertices in an undirected graph with weighted edges describing the relationships between entities. In many real...
Wei Tang, Zhengdong Lu, Inderjit S. Dhillon
CIDM
2009
IEEE
14 years 3 months ago
Mining for insider threats in business transactions and processes
—Protecting and securing sensitive information are critical challenges for businesses. Deliberate and intended actions such as malicious exploitation, theft or destruction of dat...
William Eberle, Lawrence B. Holder
KDD
2007
ACM
144views Data Mining» more  KDD 2007»
14 years 9 months ago
Fast direction-aware proximity for graph mining
In this paper we study asymmetric proximity measures on directed graphs, which quantify the relationships between two nodes or two groups of nodes. The measures are useful in seve...
Hanghang Tong, Christos Faloutsos, Yehuda Koren