Name
Title | ||
|---|---|---|
Certified Robustness of Graph Classification against Topology Attack with Randomized Smoothing |
Abstract | ||
|---|---|---|
Graph classification has practical applications in diverse fields. Recent studies show that graph-based machine learning models are especially vulnerable to adversarial perturbations due to the non i.i.d nature of graph data. By adding or deleting a small number of edges in the graph, adversaries could greatly change the graph label predicted by a graph classification model. In this work, we propose to build a smoothed graph classification model with certified robustness guarantee. We have proven that the resulting graph classification model would output the same prediction for a graph under $l_0$ bounded adversarial perturbation. We also evaluate the effectiveness of our approach under graph convolutional network (GCN) based multi-class graph classification model. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/GLOBECOM42002.2020.9322576 | GLOBECOM |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhidong Gao | 1 | 1 | 0.35 |
| Rui Hu | 2 | 97 | 15.98 |
| Yanmin Gong | 3 | 133 | 16.82 |