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ornl-hydragnn-graph-generative-models

Graph generative models using HydraGNN as neural network architecture

https://github.com/ornl/ornl-hydragnn-graph-generative-models

Science Score: 62.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    2 of 4 committers (50.0%) from academic institutions
  • ✓
    Institutional organization owner
    Organization ornl has institutional domain (software.ornl.gov)
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (10.8%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

Graph generative models using HydraGNN as neural network architecture

Basic Info
  • Host: GitHub
  • Owner: ORNL
  • License: bsd-3-clause
  • Language: Python
  • Default Branch: main
  • Size: 364 KB
Statistics
  • Stars: 0
  • Watchers: 6
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Created over 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme Contributing License Citation

README.md

Diffusion Models on Graphs with HydraGNN

This project builds on HydraGNN, leveraging its powerful GNN and ML utilities for training, testing, and model optimization.

Features

  • TBD

Quick Start

Clone the repo:

bash git clone <tbd> cd <tbd>

Install Dependencies:

Make sure you have the HydraGNN environment set up: bash pip install -r requirements.txt

Run Training:

bash python <tbd>

How It Works

HydraGNN integration: We utilize the operational utilities from HydraGNN, such as model training, testing, and optimization, to simplify workflow. Diffusion Process: Modeled on graph structures to simulate the propagation of information or features across the graph nodes. Perfect for dynamic systems! Model Parallelization: Thanks to HydraGNN, training large models with multi-GPU support is integrated.

️Configuration

All model and training parameters can be easily set via our config.json file:

json model: type: diffusion_gnn layers: 5 hidden_dim: 128 train: epochs: 100 batch_size: 32 learning_rate: 0.001

Modules

src/<>.py:

Performance

Our diffusion-enhanced GNNs show promising results in tasks such as:

Contributing

We welcome contributions! If you're interested in extending the diffusion model or improving performance, feel free to submit a pull request or open an issue.

Owner

  • Name: Oak Ridge National Laboratory
  • Login: ORNL
  • Kind: organization
  • Email: software@ornl.gov
  • Location: Oak Ridge TN

Software repositories from Oak Ridge National Laboratory

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Lupo Pasini"
  given-names: "Massimiliano"
  orcid: "https://orcid.org/0000-0002-4980-6924"
- family-names: "Reeve"
  given-names: "Samuel Temple"
  orcid: "https://orcid.org/0000-0002-4250-9476"
- family-names: "Zhang"
  given-names: "Pei"
  orcid: "https://orcid.org/0000-0002-8351-0529"
- family-names: "Choi"
  given-names: "Jong Youl"
  orcid: "https://orcid.org/0000-0002-6459-6152"
title: "HydraGNN - Distributed PyTorch implementation of multi-headed graph convolutional neural networks"
version: 1.0.0
doi: 10.11578/dc.20211019.2
date-released: 2021-10-19
url: "https://github.com/ORNL/HydraGNN"

GitHub Events

Total
  • Delete event: 1
  • Member event: 4
  • Push event: 29
  • Pull request event: 11
  • Fork event: 1
  • Create event: 5
Last Year
  • Delete event: 1
  • Member event: 4
  • Push event: 29
  • Pull request event: 11
  • Fork event: 1
  • Create event: 5

Committers

Last synced: 12 months ago

All Time
  • Total Commits: 86
  • Total Committers: 4
  • Avg Commits per committer: 21.5
  • Development Distribution Score (DDS): 0.453
Past Year
  • Commits: 83
  • Committers: 3
  • Avg Commits per committer: 27.667
  • Development Distribution Score (DDS): 0.434
Top Committers
Name Email Commits
zachfox z****x@g****m 47
bbhaduri b****3@g****u 35
Yeats, Eric y****c@o****v 3
Massimiliano Lupo Pasini m****i@g****m 1
Committer Domains (Top 20 + Academic)
ornl.gov: 1 gatech.edu: 1

Issues and Pull Requests

Last synced: 10 months ago

All Time
  • Total issues: 0
  • Total pull requests: 11
  • Average time to close issues: N/A
  • Average time to close pull requests: 3 minutes
  • Total issue authors: 0
  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 10
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 11
  • Average time to close issues: N/A
  • Average time to close pull requests: 3 minutes
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 10
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
Pull Request Authors
  • zachfox (12)
Top Labels
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