PyGIP is a comprehensive Python library focused on model extraction attacks and defenses in Graph Neural Networks (GNNs). Built on PyTorch, PyTorch Geometric, and DGL, the library offers a robust framework for understanding, implementing, and defending against attacks targeting GNN models. Check our Github Repo at https://github.com/LabRAI/PyGIP

PyOD includes more than 50 detection algorithms, from classical LOF (SIGMOD 2000) to the cutting-edge ECOD and DIF (TKDE 2022 and 2023). Since 2017, PyOD has been successfully used in numerous academic research projects and commercial products with more than 32 million downloads.

What if large language models (LLMs) could transform how we understand politics? This project introduces Political-LLM, the first comprehensive framework to systematically integrate LLMs into political science research. It bridges computer science and political science by providing a taxonomy, technical insights, and ethical considerations to guide impactful LLM applications in the political domain.

PyGDebias aims to provide a systematic schema to load datasets and compare different debiasing techniques for graph learning algorithms. Specifically, 26 graph datasets (including 24 commonly used ones and two newly constructed ones, AMiner-L and AMiner-S) are collected, and 13 algorithms are implemented in this library.