Approximation of graph edit distance based on Hausdorff matching

By Andreas Fischer, Ching Yee Suen, Volkmar Frinken, Kaspar Riesen, Horst Bunke. 2015

136 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tingting Chen
2. Yang Qiao
3. Yizhou Sun
4. Wei Wang
5. Haoyang Ding
6. Yunsheng Bai
7. Ken Gu
8. Agustin Marinovic

In Computer Vision and Pattern Recognition

1. Tingting Chen
2. Yang Qiao
3. Yizhou Sun
4. Wei Wang
5. Haoyang Ding
6. Yunsheng Bai
7. Ken Gu
8. Agustin Marinovic

Citations in the corpus

• Unsupervised Inductive Whole-Graph Embedding by Preserving Graph Proximity (2019, background): “• HED (Fischer et al., 2015): It is another approximate GED solver running in quadratic time, but instead yields lower bounds of exact GEDs.”
• Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity (2019, methodology): “B utilizes the ground-truth GED results collectively determined by BEAM (Neuhaus et al., 2006), HUNGARIAN (Riesen & Bunke, 2009), and VJ (Fankhauser et al., 2011). UGRAPHEMB even outperforms HED (Fischer et al., 2015), a state-of-the-art approximate GED computation algorithm, under most settings”