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ICDM
2008
IEEE

Metropolis Algorithms for Representative Subgraph Sampling

10 years 9 months ago
Metropolis Algorithms for Representative Subgraph Sampling
While data mining in chemoinformatics studied graph data with dozens of nodes, systems biology and the Internet are now generating graph data with thousands and millions of nodes. Hence data mining faces the algorithmic challenge of coping with this significant increase in graph size: Classic algorithms for data analysis are often too expensive and too slow on large graphs. While one strategy to overcome this problem is to design novel efficient algorithms, the other is to ’reduce’ the size of the large graph by sampling. This is the scope of this paper: We will present novel Metropolis algorithms for sampling a ’representative’ small subgraph from the original large graph, with ’representative’ describing the requirement that the sample shall preserve crucial graph properties of the original graph. In our experiments, we improve over the pioneering work of Leskovec and Faloutsos (KDD 2006), by producing representative subgraph samples that are both smaller and of higher...
Christian Hübler, Hans-Peter Kriegel, Karsten
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICDM
Authors Christian Hübler, Hans-Peter Kriegel, Karsten M. Borgwardt, Zoubin Ghahramani
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