TY - GEN
T1 - A mixed entropy local-global reproducing kernel for attributed graphs
AU - Cui, Lixin
AU - Bai, Lu
AU - Rossi, Luca
AU - Zhang, Zhihong
AU - Xu, Lixiang
AU - Hancock, Edwin R.
PY - 2018/8/2
Y1 - 2018/8/2
N2 - In this paper, we develop a new mixed entropy local-global reproducing kernel for vertex attributed graphs based on depth-based representations that naturally reflect both local and global entropy based graph characteristics. Specifically, for a pair of graphs, we commence by computing the nest depth-based representations rooted at the centroid vertices. The resulting mixed local-global reproducing kernel for a pair of graphs is computed by measuring a basic H1-reproducing kernel between their nest representations associated with different entropy measures. We show that the proposed kernel not only reflect both the local and global graph characteristics through the nest depth-based representations, but also reflect rich edge connection information and vertex label information through different kinds of entropy measures. Moreover, since both the required basic H1-reproducing kernel and the nest depth-based representation can be computed in a polynomial time, the new proposed kernel processes efficient computational complexity. Experiments on standard graph datasets demonstrate the effectiveness and efficiency of the proposed kernel.
AB - In this paper, we develop a new mixed entropy local-global reproducing kernel for vertex attributed graphs based on depth-based representations that naturally reflect both local and global entropy based graph characteristics. Specifically, for a pair of graphs, we commence by computing the nest depth-based representations rooted at the centroid vertices. The resulting mixed local-global reproducing kernel for a pair of graphs is computed by measuring a basic H1-reproducing kernel between their nest representations associated with different entropy measures. We show that the proposed kernel not only reflect both the local and global graph characteristics through the nest depth-based representations, but also reflect rich edge connection information and vertex label information through different kinds of entropy measures. Moreover, since both the required basic H1-reproducing kernel and the nest depth-based representation can be computed in a polynomial time, the new proposed kernel processes efficient computational complexity. Experiments on standard graph datasets demonstrate the effectiveness and efficiency of the proposed kernel.
KW - Attributed graphs
KW - Entropy
KW - Local-global graph kernels
UR - http://www.scopus.com/inward/record.url?scp=85052211769&partnerID=8YFLogxK
UR - https://link.springer.com/chapter/10.1007%2F978-3-319-97785-0_48
U2 - 10.1007/978-3-319-97785-0_48
DO - 10.1007/978-3-319-97785-0_48
M3 - Conference publication
AN - SCOPUS:85052211769
SN - 9783319977843
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 501
EP - 511
BT - Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, S+SSPR 2018, Proceedings
PB - Springer
T2 - Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition, SSPR 2018 and Statistical Techniques in Pattern Recognition, SPR 2018
Y2 - 17 August 2018 through 19 August 2018
ER -