graph-ml
Datasets
All datasets matching “graph-ml”edge-ML-metabolomics-graph
edge_ML metabolomics co-response graph
An undirected graph of 18,494 nodes and 2,709,209 edges built from pairwise
metabolite co-response statistics across 83 MetaboLights
studies, together with the node properties, the PyTorch Geometric graph object, and the
full pipeline that produces them.
A node is one differential comparison within one study assay (MTBLS1405_0002_00003332
= study MTBLS1405, assay 002, feature 00003332). An edge carries the association
between two… See the full description on the dataset page: https://huggingface.co/datasets/kozo2/edge-ML-metabolomics-graph.china-city-graphml
VisNavMap City GraphML Snapshots
This dataset contains the city-level navigation graph cache derived from the
city-level OpenStreetMap PBF files released in the Hugging Face dataset
86Cao/china-city-osm-pbf.
It is intended to make
VisNavMap training, rollout, and evaluation reproducible without rebuilding a
graph from a PBF at runtime.
Data provenance
The data-generation chain is:
Hugging Face: 86Cao/china-city-osm-pbf
│
│ download city-level… See the full description on the dataset page: https://huggingface.co/datasets/86Cao/china-city-graphml.Graph-MLLMclak-consulting-graph-ml
CLAK Consulting Knowledge Graph Dataset
Graph-ML dataset for consulting knowledge representation learning.
Dataset Structure
Inspired by OGB (Open Graph Benchmark) format:
Field
Type
Description
node_feat
list[list[int]]
Node features (type, one-hot)
edge_index
list[tuple[int,int]]
Edge pairs (source, target)
edge_attr
list[int]
Edge types
y
list[float]
Target labels
num_nodes
int
Node count
domain
str
Consulting domain
Node Types… See the full description on the dataset page: https://huggingface.co/datasets/Kraft102/clak-consulting-graph-ml.FavaMultiSamples
FavaMultiSamples
This repository contains the FavaMultiSamples dataset proposed and used in the research paper titled
"FactSelfCheck: Fact-Level Black-Box Hallucination Detection for LLMs".
Citation
If you use this repository in your work, please cite it as follows:
@misc{sawczyn2025factselfcheckfactlevelblackboxhallucination,
title={FactSelfCheck: Fact-Level Black-Box Hallucination Detection for LLMs},
author={Albert Sawczyn and Jakub Binkowski and Denis… See the full description on the dataset page: https://huggingface.co/datasets/graphml-lab-pwr/FavaMultiSamples.Math_Knowledge_Graph_Dataset