Community detection using random-walk similarity and application to image clustering
The technology used to detect community structures in graphs, or graph clustering technology, is important in a wide range of disciplines, such as sociology, biology, and computer science. Previously, many successful community detection methods have relied on the optimization of a quantity referred to as modularity, which is a quality index for the partition of a graph into communities. However, such methods suffer from a key drawback, namely, the inability to identify relatively small communities. To overcome this drawback, we propose a novel community detection method that can detect small communities. This is based on the property that a random walker will not readily leave a community even if it is small. The work presented in this paper demonstrates that our method detects both small and large communities in the practical application of clustering tourist attraction images obtained from Flickr.