TY - GEN
T1 - Graph embedding using multi-layer adjacent point merging model
AU - Huang, Jianming
AU - Kasai, Hiroyuki
N1 - Funding Information:
∗H. Kasai was partially supported by JSPS KAKENHI Grant Numbers JP16K00031 and JP17H01732, and by Support Center for Advanced Telecomm. Technology Research (SCAT).
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - For graph classification tasks, many traditional kernel methods focus on measuring the similarity between graphs. These methods have achieved great success on resolving graph isomorphism problems. However, in some classification problems, the graph class depends on not only the topological similarity of the whole graph, but also constituent subgraph patterns. To this end, we propose a novel graph embedding method using a multi-layer adjacent point merging model. This embedding method allows us to extract different subgraph patterns from train-data. Then we present a flexible loss function for feature selection which enhances the robustness of our method for different classification problems. Finally, numerical evaluations demonstrate that our proposed method outperforms many state-of-the-art methods.
AB - For graph classification tasks, many traditional kernel methods focus on measuring the similarity between graphs. These methods have achieved great success on resolving graph isomorphism problems. However, in some classification problems, the graph class depends on not only the topological similarity of the whole graph, but also constituent subgraph patterns. To this end, we propose a novel graph embedding method using a multi-layer adjacent point merging model. This embedding method allows us to extract different subgraph patterns from train-data. Then we present a flexible loss function for feature selection which enhances the robustness of our method for different classification problems. Finally, numerical evaluations demonstrate that our proposed method outperforms many state-of-the-art methods.
KW - Graph classification
KW - Graph embedding
KW - Graph kernel
UR - http://www.scopus.com/inward/record.url?scp=85103291312&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85103291312&partnerID=8YFLogxK
U2 - 10.1109/ICASSP39728.2021.9413362
DO - 10.1109/ICASSP39728.2021.9413362
M3 - Conference contribution
AN - SCOPUS:85103291312
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3585
EP - 3589
BT - 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021
Y2 - 6 June 2021 through 11 June 2021
ER -