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» Graph mining: Laws, generators, and algorithms
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ICS
2010
Tsinghua U.
14 years 5 months ago
Local Algorithms for Finding Interesting Individuals in Large Networks
: We initiate the study of local, sublinear time algorithms for finding vertices with extreme topological properties -- such as high degree or clustering coefficient -- in large so...
Mickey Brautbar, Michael Kearns
AUSDM
2006
Springer
93views Data Mining» more  AUSDM 2006»
13 years 11 months ago
Visualisation and Exploration of Scientific Data Using Graphs
Abstract. We present a prototype application for graph-based data exploration and mining, with particular emphasis on scientific data. The application has a Flash-based graphical i...
Ben Raymond, Lee Belbin
KDD
2009
ACM
179views Data Mining» more  KDD 2009»
14 years 2 months ago
Identifying graphs from noisy and incomplete data
There is a growing wealth of data describing networks of various types, including social networks, physical networks such as transportation or communication networks, and biologic...
Galileo Mark S. Namata Jr., Lise Getoor
KDD
2006
ACM
164views Data Mining» more  KDD 2006»
14 years 8 months ago
Sampling from large graphs
Given a huge real graph, how can we derive a representative sample? There are many known algorithms to compute interesting measures (shortest paths, centrality, betweenness, etc.)...
Jure Leskovec, Christos Faloutsos
SDM
2004
SIAM
163views Data Mining» more  SDM 2004»
13 years 9 months ago
Basic Association Rules
Previous approaches for mining association rules generate large sets of association rules. Such sets are difficult for users to understand and manage. Here, the concept of a restri...
Guichong Li, Howard J. Hamilton