Graph Mining Framework for Finding and Visualizing Substructures Using Graph Database

In the scientific and commercial domains, graph as a data structure has become increasingly important for modeling sophisticated structures especially the interactions within them. Mining the knowledge from graph data has become a major research topic in recent data mining studies. Researchers have...

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Bibliographic Details
Main Authors: Shrivastava, S., Pal, S.N.
Format: Conference Proceeding
Language:English
Subjects:
Computer networks
Data mining
Data structures
Data visualization
dense substructure extraction
frequent substructure discovery
graph database
graph mining
graph visualization
Relational databases
Social network services
Software engineering
Transaction databases
Visual databases
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Summary:In the scientific and commercial domains, graph as a data structure has become increasingly important for modeling sophisticated structures especially the interactions within them. Mining the knowledge from graph data has become a major research topic in recent data mining studies. Researchers have designed several efficient algorithms for mining various substructures (subgraphs) within the graph. Several graph visualization tools and techniques exist. But there is a need to define a unified framework for finding and visualizing substructures from graph. In this paper we propose a graph mining framework that captures entities and relations between entities from different data sources. The framework further models this data as a graph and facilitates the dense substructure extraction and frequent substructure discovery in order to find substructures. It also supports knowledge visualization using graphs.
DOI:10.1109/ASONAM.2009.16