Abstract: 

Graph Neural Networks (GNNs) have emerged as prominent techniques within the field of recommendation systems. Due to the intricate nature of GNNs and their essential role in conveying recommendation outcomes to users while ensuring algorithmic fairness and minimizing biases, there is a pressing demand to enhance the interpretability and resilience of these approaches.

Among the strategies aimed at achieving this, counterfactual explanation stands out as a pivotal approach, aligning its objectives closely with those of adversarial examples. Both methodologies share a fundamental goal: to alter the model's output with minimal perturbations. Within this context, our reproducibility study undertakes a comparative analysis of leading methodologies in these two domains.

This talk aim to elucidate the distinctive traits of models operating in these realms, pinpointing their shared applications and potential synergies. Our ultimate objective is to uncover and explore the interconnectedness of these techniques, thereby fostering a deeper understanding of their combined utility and implications.