EGC2: Enhanced Graph Classification with Easy Graph Compression

Graph classification plays a significant role in network analysis. It also faces potential security threat like adversarial attacks. Some defense methods may sacrifice algorithm complexity for robustness like adversarial training, while others may sacrifice the clean example performance such as smoothing-based defense. Most of them are suffered from high-complexity or less transferability. To address this problem, we proposed EGC$^2$, an enhanced graph classification model with easy graph compression. EGC$^2$ captures the relationship between features of different nodes by constructing feature graphs and improving aggregate node-level representation. To achieve lower complexity defense applied to various graph classification models, EGC$^2$ utilizes a centrality-based edge importance index to compress graphs, filtering out trivial structures and even adversarial perturbations of the input graphs, thus improves its robustness. Experiments on seven benchmark datasets demonstrate that the proposed feature read-out and graph compression mechanisms enhance the robustness of various basic models, thus achieving the state-of-the-art performance of accuracy and robustness in the threat of different adversarial attacks.