TY - GEN
T1 - An image-based representation for graph classification
AU - Rayar, Frédéric
AU - Uchida, Seiichi
N1 - Funding Information:
Acknowledgement. The authors would like to give credits to the organisers of the Graph Distance Contest, who provided the challenge data sets and the results of the second challenge. This research was partially supported by MEXT-Japan (Grant No. 17H06100).
Funding Information:
The authors would like to give credits to the organisers of the Graph Distance Contest, who provided the challenge data sets and the results of the second challenge. This research was partially supported by MEXT-Japan (Grant No. 17H06100).
Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - This paper proposes to study the relevance of image representations to perform graph classification. To do so, the adjacency matrix of a given graph is reordered using several matrix reordering algorithms. The resulting matrix is then converted into an image thumbnail, that is used to represent the graph. Experimentation on several chemical graph data sets and an image data set show that the proposed graph representation performs as well as the state-of-the-art methods.
AB - This paper proposes to study the relevance of image representations to perform graph classification. To do so, the adjacency matrix of a given graph is reordered using several matrix reordering algorithms. The resulting matrix is then converted into an image thumbnail, that is used to represent the graph. Experimentation on several chemical graph data sets and an image data set show that the proposed graph representation performs as well as the state-of-the-art methods.
UR - http://www.scopus.com/inward/record.url?scp=85052225985&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85052225985&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-97785-0_14
DO - 10.1007/978-3-319-97785-0_14
M3 - Conference contribution
AN - SCOPUS:85052225985
SN - 9783319977843
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 140
EP - 149
BT - Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, S+SSPR 2018, Proceedings
A2 - Hancock, Edwin R.
A2 - Ho, Tin Kam
A2 - Biggio, Battista
A2 - Wilson, Richard C.
A2 - Robles-Kelly, Antonio
A2 - Bai, Xiao
PB - Springer Verlag
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 -