Hardness of Approximating Graph Transformation Problem

By Chih-Long Lin. 1994

How researchers see this paper

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

1. William L. Hamilton
2. Zhe Gan
3. Jianwei Yang
4. Jiaxuan You
5. Devi Parikh
6. Yoshua Bengio
7. Jure Leskovec
8. Rex Ying

Citations in the corpus

• GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models (2018, background): “…kernel Hilbert space (RKHS) H is used as its function class F , and k is the associated kernel, the squared MMD between two sets of samples from distributions p and q can be derived as (Gretton et al., 2012) Proper distance metrics over graphs are in general computationally intractable (Lin, 1994).”
• Graph R-CNN for Scene Graph Generation (2018, background): “We note that graph similarity based on minimum graph edit distance has been well-studied in graph theory [5]; however, computing exact solution is NP-complete and approximation APX-hard [20].”
• GraphRNN: A Deep Generative Model for Graphs (2018, methodology): “(10) 6We also attempted using degree and clustering coefficients as features for nodes, but did not achieve better performance Proper distance metrics over graphs are in general computationally intractable (Lin, 1994).”