Subgraph Similarity Search in Large Graphs

Kanigalpula, S (2015) Subgraph Similarity Search in Large Graphs. Masters thesis, Indian Institute of Technology Hyderabad.

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One of the major challenges in applications related to social networks, computational biology, collaboration networks etc., is to efficiently search for similar patterns in their underlying graphs. These graphs are typically noisy and contain thousands of vertices and millions of edges. In many cases, the graphs are unlabelled and the notion of similarity is also not well defined. We study the problem of searching an induced sub graph in a large target graph that is most similar to the given query graph. We assume that the query graph and target graph are undirected and unlabelled. We use graph let kernels [1] to define graph similarity. Graph let kernels are known to perform better than other kernels in different applications.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Thesis (Masters)
Uncontrolled Keywords: Subgraph Similarity, Graph let Kernels, TD311
Subjects: Computer science > Big Data Analytics
Divisions: Department of Computer Science & Engineering
Depositing User: Library Staff
Date Deposited: 29 Jun 2015 10:37
Last Modified: 13 May 2019 11:13
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