An Improved Random Walk Algorithm Based on Correlation Coefficient to Find Scientific Communities
Abstract: With the growth of publishing scientific literature on the World Wide Web, there is a great demand for clustering online scientific literature by using the citation patterns. A scientific community in the citation graph represents related papers on a single topic. In this paper we improve the random walk graph clustering algorithm to find scientific communities by using correlation coefficient in the citation graph as the similarity metric. Our experiment results show that the approach performs better than the original random walk graph clustering method.
Loading