Abstract
Detection of sub-graphs in community graphs is an important task and useful for characterizing community graphs. This characterization leads to classification as well as clusterings of community graphs. It also leads to finding differences among a set of community graphs as well as buildings of indices of community graphs. Finally, this characterization leads discovery of knowledge from sub-graphs. This proposed approach of detection of a sub-community graph from a group of community graphs using simple graph theory techniques. So, that knowledge could be discovered from the sub-community graph detected in a set of community graphs. The proposed algorithm has been implemented with two examples including one benchmark network and observed satisfactory results.
Highlights
Discovering a frequent sub-graph from a group of graphs is said to be a graph pattern. That these sub-graphs are useful for building community graph indices, classification and clustering, and finding differences among a group of community graphs
The discovery of frequent sub-community graph problems leads discovery of frequent sub-community graphs in a group of community graphs. That these frequent sub-community graphs are useful in data analysis and data mining for similarity search in databases of community graph, clustering, classification of community graphs, indexing of community graphs, etc
This proposed and revised algorithm is for the detection of a sub-community graph in ‘n’ number of community graphs using graph mining [13]
Summary
Discovering a frequent sub-graph from a group of graphs is said to be a graph pattern That these sub-graphs are useful for building community graph indices, classification and clustering, and finding differences among a group of community graphs. The discovery of frequent sub-community graph problems leads discovery of frequent sub-community graphs in a group of community graphs That these frequent sub-community graphs are useful in data analysis and data mining for similarity search in databases of community graph, clustering, classification of community graphs, indexing of community graphs, etc. This proposed and revised algorithm is for the detection of a sub-community graph in ‘n’ number of community graphs using graph mining [13]
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