StellarGraph - Machine Learning on Graphs
-
Updated
Apr 10, 2024 - Python
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.
This one weird trick turns JSON documents into semantic graph databases!
NebulaGraph DGL(Deep Graph Library) Integration Package. (WIP)
Pipeline for analyzing fraud in card transaction data-sets with an addition of graph features, modeled using Random Forest
AdvImmune model from "Adversarial Immunization for Certifiable Robustness on Graphs" (WSDM 2021)
G-NIA model from "Single Node Injection Attack against Graph Neural Networks" (CIKM 2021)
Converts Mizar ESX MML mathematical data to property graph formats - GraphML, YARS-PG for Neo4j, and other graph databases. Supports external RDF data.
Extracting graph data from smart contract source code
Map articles metadata and relationship to schema.org entities and stores them in graph database
Code for TDA on graph data
Poperty graphs modelling in neo4j and Python. Team project from UPC's Master's Degree in Data Science
Efficient graph data encoding for neural network consumption. An implementation of Graph DNA (Liwei Wu et. al).
Introduction to Graph Databases based off The Semantic Web 3.0 and RDF
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.
StellarGraph - Machine Learning on Graphs
Domain-Specific Search Solutions using LLMs and KGs.
Add a description, image, and links to the graph-data topic page so that developers can more easily learn about it.
To associate your repository with the graph-data topic, visit your repo's landing page and select "manage topics."