metadata
title: CrediNet
colorFrom: indigo
colorTo: purple
sdk: static
pinned: false
short_description: Network-based Credibility Modelling
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).
Projects
- CrediBench: benchmark of billion-scale temporal webgraphs on a monthly granularity, sourced from Common Crawl. For the corresponding graph construction pipeline refer to CrediGraph - GitHub.
- CrediPred: inferred scores from our developed model (for more details on the model architecture, refer to CrediPred - GitHub).
- CrediText: text embeddings extracted from scraped web content. Find the corresponding scraping and embedding pipelines on CrediText - GitHub.
- CrediNet: API set-up to query CrediPred scores easily on the client side (for more details on the API set up and examples usages, refer to CrediNet - GitHub).
