TY - GEN
T1 - Cheetah
T2 - SIAM International Conference on Data Mining 2015, SDM 2015
AU - Li, Liangyue
AU - Tong, Hanghang
AU - Xiao, Yanghua
AU - Fan, Wei
N1 - Funding Information:
Acknowledgment: This material is supported by the National Science Foundation under Grant No. IIS1017415, by the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053, by Defense Advanced Research Projects Agency (DARPA) under Contract Number W911NF-11-C-0200 and W911NF-12-C-0028, by National Institutes of Health under the grant number R01LM011986, Region II University Transportation Center under the project number 49997-33 25. Yanghua Xiao was partially supported by the National NSFC(No.61472085, 61171132, 61033010), by National Key Basic Research Program of China under No.2015CB358800, by Shanghai STCF under No. 13511505302, by NSF of Jiangsu Prov. under No. BK2010280.
Publisher Copyright:
Copyright © SIAM.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84961926584&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84961926584&partnerID=8YFLogxK
U2 - 10.1137/1.9781611974010.32
DO - 10.1137/1.9781611974010.32
M3 - Conference contribution
AN - SCOPUS:84961926584
T3 - SIAM International Conference on Data Mining 2015, SDM 2015
SP - 280
EP - 288
BT - SIAM International Conference on Data Mining 2015, SDM 2015
A2 - Venkatasubramanian, Suresh
A2 - Ye, Jieping
PB - Society for Industrial and Applied Mathematics Publications
Y2 - 30 April 2015 through 2 May 2015
ER -