Abstract
Random walk graph kernel has been used as an important tool for various data mining tasks including classification and similarity computation. Despite its usefulness, however, it suffers from the expensive computational cost which is at least O(n3) or O(m2) for graphs with n nodes and m edges. In this paper, we propose ARK, a set of fast algorithms for random walk graph kernel computation. ARK is based on the observation that real graphs have much lower intrinsic ranks, compared with the orders of the graphs. ARK exploits the low rank structure to quickly compute random walk graph kernels in O(n2) or O(m) time. Experimental results show that our method is up to 97,865× faster than the existing algorithms, while providing more than 91.3% of the accuracies.
Submitted Version (
Free)
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
