ICASSP2021 “Graph embedding using multi-layer adjacent point merging model”
6月6日から11日にトロント（カナダ）で開催される信号処理分野・トップ会議 IEEE 46th International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2021 に1件の研究論文が採択されました．
Graph embedding using multi-layer adjacent point merging model [paper (publisher’s site), arXiv preprint 2010.14773]
- Authors: J. Huang (M1, 1st-year graduate student) and HK
- Abstract: 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.