CrediNet

CrediNet is set of tools that use graph machine learning and 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.

Projects

• CrediBench: A web-scale temporal graph generation pipeline to analyse misinformation at scale based on structural, temporal and textual signals