StellarGraph - Machine Learning on Graphs

What's in your data? Extract schema, statistics and entities from datasets

A curated collection of adversarial attack and defense on graph data.

Making RDF easy enough for most developers

NebulaGraph DGL(Deep Graph Library) Integration Package. (WIP)

This one weird trick turns JSON documents into semantic graph databases!

G-NIA model from "Single Node Injection Attack against Graph Neural Networks" (CIKM 2021)

Domain-Specific Search Solutions using LLMs and KGs.

Extracting graph data from smart contract source code

AdvImmune model from "Adversarial Immunization for Certifiable Robustness on Graphs" (WSDM 2021)

Converts Mizar ESX MML mathematical data to property graph formats - GraphML, YARS-PG for Neo4j, and other graph databases. Supports external RDF data.

Python implementation of BGSR. BGSR is a method using functional brain data with the aim to boost neurological disorder diagnosis. It learns how to generate high-resolution (HR) graphs from low-resolution (LR) graphs without resorting to the computationally expensive image processing pipelines for connectome construction at high-resolution scales.

Code for TDA on graph data

Introduction to Graph Databases based off The Semantic Web 3.0 and RDF

Poperty graphs modelling in neo4j and Python. Team project from UPC's Master's Degree in Data Science

StellarGraph - Machine Learning on Graphs

Pipeline for analyzing fraud in card transaction data-sets with an addition of graph features, modeled using Random Forest

Map articles metadata and relationship to schema.org entities and stores them in graph database

Efficient graph data encoding for neural network consumption. An implementation of Graph DNA (Liwei Wu et. al).

An interactive web app built with Streamlit to visualize tables or graphs, and execute SPARQL queries. The app is designed with a simple, user-friendly layout to make it easier to interact with and explore linked data.