Microsoft Academic Graph Information Retrieval for Research Recommendation and Assistance

In today's information-driven world, access to scientific publications has become increasingly easy. At the same time, filtering through the massive volume of available research has become more challenging than ever. Graph Neural Networks (GNNs) and graph attention mechanisms have shown strong effectiveness in searching large-scale information databases, particularly when combined with modern large language models. In this paper, we propose an Attention-Based Subgraph Retriever, a GNN-as-retriever model that applies attention-based pruning to extract a refined subgraph, which is then passed to a large language model for advanced knowledge reasoning.

Teach Me to Trick: Exploring Adversarial Transferability via Knowledge Distillation

We investigate whether knowledge distillation (KD) from multiple heterogeneous teacher models can enhance the generation of transferable adversarial examples. A lightweight student model is trained using two KD strategies: curriculum-based switching and joint optimization, with ResNet50 and DenseNet-161 as teachers. The trained student is then used to generate adversarial examples using FG, FGS, and PGD attacks, which are evaluated against a black-box target model (GoogLeNet). Our results show that student models distilled from multiple teachers achieve attack success rates comparable to ensemble-based baselines, while reducing adversarial example generation time by up to a factor of six. An ablation study further reveals that lower temperature settings and the inclusion of hard-label supervision significantly enhance transferability. These findings suggest that KD can serve not only as a model compression technique but also as a powerful tool for improving the efficiency and effectiveness of black-box adversarial attacks.