Graph kernels provide an expressive approach to measuring the similarity of two graphs, and are key building blocks behind many real-world applications, such as bioinformatics, brain science and social networks . However, current methods for computing graph kernels assume the input graphs are static, which is often not the case in reality. It is highly desirable to track the graph kernels on dynamic graphs evolving over time in a timely manner. In this paper, we propose a family of Cheetah algorithms to deal with the challenge. Cheetah leverages the low rank structure of graph updates and incrementally updates the eigen-decomposition or SVD of the adjacency matrices of graphs. Experimental evaluations on real world graphs validate our algorithms (1) are significantly faster than alternatives with high accuracy and (b) scale sub-linearly.
Cheetah REF by Li et al. tracks the graph kernel by incrementally updating the low-rank approximation on input graphs.
Cheetah: Fast graph kernel tracking on dynamic graphs
SDM
Computer Science