ObjectRank is one of the popular graph mining methods that enables us to evaluate the importance of each vertex on heterogeneous graphs. However, it is computationally expensive to apply it to large graphs since ObjectRank needs to compute the importance of all vertices iteratively. In this work, we present a fast ObjectRank algorithm, FORank, that accurately approximates the keyword search results. FORank iteratively prunes vertices whose convergence score likely has less impact on the results during iterative computation. The experiments showed that FORank runs 7 times faster than ObjectRank computation with over 90% accuracy approximation.
The demands for graph data analysis methods are increasing. RankClus is a framework to extract clusters by integrating clustering and ranking on heterogeneous graphs; it enhances the clustering results by alternately updates the results of clustering and ranking for the better understanding of the clusters. However, RankClus is computationally expensive if a graph is large since it needs to iterate both clustering and ranking for all nodes. In this paper, to address this problem, we propose a novel fast RankClus algorithm for heterogeneous graphs. To speed up the entire procedure of RankClus, our proposed algorithm reduces the computational cost of the ranking process in each iteration. Our proposal measures how each node affects the clustering result; if it is not significant, we prune the node. Furthermore, we also present a parallel algorithm by extending our proposed algorithm by fully exploiting a modern manycore CPU. As a result, our extensive evaluations clarified that our fast and parallel algorithms drastically cut off the computation time of the original algorithm RancClus.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.