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2005
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Discovering frequent topological structures from graph datasets

10 years 12 months ago
Discovering frequent topological structures from graph datasets
The problem of finding frequent patterns from graph-based datasets is an important one that finds applications in drug discovery, protein structure analysis, XML querying, and social network analysis among others. In this paper we propose a framework to mine frequent large-scale structures, formally defined as frequent topological structures, from graph datasets. Key elements of our framework include, fast algorithms for discovering frequent topological patterns based on the well known notion of a topological minor, algorithms for specifying and pushing constraints deep into the mining process for discovering constrained topological patterns, and mechanisms for specifying approximate matches when discovering frequent topological patterns in noisy datasets. We demonstrate the viability and scalability of the proposed algorithms on real and synthetic datasets and also discuss the use of the framework to discover meaningful topological structures from protein structure data.
Ruoming Jin, Chao Wang, Dmitrii Polshakov, Sriniva
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2005
Where KDD
Authors Ruoming Jin, Chao Wang, Dmitrii Polshakov, Srinivasan Parthasarathy, Gagan Agrawal
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