Network Community Detection on Metric Space
AbstractCommunity detection in a complex network is an important problem of much interest in recent years. In general, a community detection algorithm chooses an objective function and captures the communities of the network by optimizing the objective function, and then, one uses various heuristics to solve the optimization problem to extract the interesting communities for the user. In this article, we demonstrate the procedure to transform a graph into points of a metric space and develop the methods of community detection with the help of a metric defined for a pair of points. We have also studied and analyzed the community structure of the network therein. The results obtained with our approach are very competitive with most of the well-known algorithms in the literature, and this is justified over the large collection of datasets. On the other hand, it can be observed that time taken by our algorithm is quite less compared to other methods and justifies the theoretical findings. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Saha, S.; Ghrera, S.P. Network Community Detection on Metric Space. Algorithms 2015, 8, 680-696.
Saha S, Ghrera SP. Network Community Detection on Metric Space. Algorithms. 2015; 8(3):680-696.Chicago/Turabian Style
Saha, Suman; Ghrera, Satya P. 2015. "Network Community Detection on Metric Space." Algorithms 8, no. 3: 680-696.