CrediNet

CrediNet is an organization developing Graph Machine Learning-based computational methods for Credibility Modelling on the Web. We develop billion-scale data webgraphs and use them to assess credibility levels of websites, which can be used downstream to augment Retrieval-Augmented Generation robustness and fact-checking. This involves large-scale web scraping and text processing, and developing model architectures to interpret the different types of signals we can find on the web (including structural, temporal and linguistic cues).

• See also our Github codebases.