The adversarial perturbation techniques proposed in AvoGCL can be effectively extended to other types of graph data, such as social networks and citation networks, by adapting the underlying principles of dual adversarial perturbation to the unique characteristics of these graphs.
Social Networks: In social networks, nodes represent users, and edges represent relationships or interactions between them. The adversarial structure perturbator can be employed to generate low-redundancy subgraphs by selectively removing or adding edges based on the importance of relationships, which can be evaluated using a discriminator similar to that in AvoGCL. For instance, edges that represent weak or infrequent interactions could be targeted for removal, while stronger connections could be preserved or even enhanced. The adversarial embedding perturbator can then introduce variations in user embeddings to create more challenging contrastive views, thereby improving the robustness of user representations against noise and sparsity in social interactions.
Citation Networks: In citation networks, where nodes represent academic papers and edges represent citations, the adversarial perturbation techniques can be adapted to focus on the semantic relevance of citations. The structure perturbator could remove citations that are less relevant or frequently cited, while the embedding perturbator could introduce variations based on the content or context of the papers. This would allow for the generation of contrastive views that emphasize the most significant relationships and enhance the learning of paper representations, ultimately improving recommendation systems for academic papers or citation prediction tasks.
Generalization to Other Graph Types: The core idea of generating contrastive views through adversarial perturbations can be generalized to any graph-based data structure. By identifying the key relationships and attributes that define the graph's structure, similar perturbation strategies can be employed to create more informative and challenging views. This adaptability makes the dual adversarial approach a versatile tool for enhancing representation learning across various domains.