Welcome to the InfoSci Platform
Some Other Applications in Community Graph under the Preview of Social Graph Using Graph-Mining Techniques

Some Other Applications in Community Graph under the Preview of Social Graph Using Graph-Mining Techniques

Bapuji Rao, Sasmita Mishra, Saroja Nanda Mishra
Copyright: © 2017 |Pages: 61
ISBN13: 9781522518778|ISBN10: 1522518770|EISBN13: 9781522518785
DOI: 10.4018/978-1-5225-1877-8.ch014
Cite Chapter Cite Chapter

MLA

Rao, Bapuji, et al. "Some Other Applications in Community Graph under the Preview of Social Graph Using Graph-Mining Techniques." Web Data Mining and the Development of Knowledge-Based Decision Support Systems, edited by G. Sreedhar, IGI Global, 2017, pp. 223-283. https://doi.org/10.4018/978-1-5225-1877-8.ch014

APA

Rao, B., Mishra, S., & Mishra, S. N. (2017). Some Other Applications in Community Graph under the Preview of Social Graph Using Graph-Mining Techniques. In G. Sreedhar (Ed.), Web Data Mining and the Development of Knowledge-Based Decision Support Systems (pp. 223-283). IGI Global. https://doi.org/10.4018/978-1-5225-1877-8.ch014

Chicago

Rao, Bapuji, Sasmita Mishra, and Saroja Nanda Mishra. "Some Other Applications in Community Graph under the Preview of Social Graph Using Graph-Mining Techniques." In Web Data Mining and the Development of Knowledge-Based Decision Support Systems, edited by G. Sreedhar, 223-283. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1877-8.ch014

Export Reference

Mendeley
Favorite

Abstract

The retrieval of sub-graph from a large graph in structured data mining is one of the fundamental tasks for analyze. Visualization and analyze large community graph are challenging day by day. Since a large community graph is very difficult to visualize, so compression is essential. To study a large community graph, compression technique may be used for compression of community graph. There should not be any loss of information or knowledge while compressing the community graph. Similarly to extract desired knowledge of a particular sub-graph from a large community graph, then the large community graph needs to be partitioned into smaller sub-community graphs. The partition aims at the edges among the community members of dissimilar communities in a community graph. Sometimes it is essential 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 from both which leads saving of time.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.