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DOI: 10.14569/IJACSA.2016.070563
PDF

An Approach to Finding Similarity Between Two Community Graphs Using Graph Mining Techniques

Author 1: Bapuji Rao
Author 2: Saroja Nanda Mishra

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 5, 2016.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Graph similarity has studied in the fields of shape retrieval, object recognition, face recognition and many more areas. Sometimes it is important to compare two community graphs for similarity which makes easier for mining the reliable knowledge from a large community graph. Once the similarity is done then, the necessary mining of knowledge can be extracted from only one community graph rather than both which leads saving of time. This paper proposes an algorithm for similarity check of two community graphs using graph mining techniques. Since a large community graph is difficult to visualize, so compression is essential. This proposed method seems to be easier and faster while checking for similarity between two community graphs since the comparison is between the two compressed community graphs rather than the actual large community graphs.

Keywords: community graph; compressed community graph; dissimilar edges; self-loop; similar edges; weighted adjacency matrix

Bapuji Rao and Saroja Nanda Mishra, “An Approach to Finding Similarity Between Two Community Graphs Using Graph Mining Techniques” International Journal of Advanced Computer Science and Applications(IJACSA), 7(5), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070563

@article{Rao2016,
title = {An Approach to Finding Similarity Between Two Community Graphs Using Graph Mining Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070563},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070563},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {5},
author = {Bapuji Rao and Saroja Nanda Mishra}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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