arminmehrabian/nasa-eosdis-heterogeneous-gnn

EOSDIS Graph Neural Network Model Card

Model Overview

Model Name: EOSDIS-GNN Version: 1.0.4 Type: Heterogeneous Graph Neural Network Framework: PyTorch + PyTorch Geometric Base Language Model: nasa-impact/nasa-smd-ibm-st-v2

This model was trained on 2025-09-10.

Model Details

• —Hidden Channels: 256
• —Number of Layers: 3
• —Convolution Type: sage
• —Max Epochs: 100000

Core Components

• —Base Text Encoder: NASA-SMD-IBM Language Model (768-dimensional embeddings)
• —Graph Neural Network: Heterogeneous GNN with multiple layers
• —Node Types: Dataset, Publication, Instrument, Platform, ScienceKeyword
• —Edge Types: Multiple relationship types between nodes

Technical Specifications

• —Input Dimensions: 768 (NASA-SMD-IBM embeddings)
• —Hidden Dimensions: Configurable (default: 256)
• —Output Dimensions: 768 (aligned with NASA-SMD-IBM space)
• —Number of Layers: Configurable (default: 3)
• —Activation Function: ReLU
• —Dropout: Applied between layers

Training Details

Training Data

• —Source: NASA EOSDIS Knowledge Graph
• —Node Types and Counts:
• —Datasets: Earth science datasets from NASA DAACs
• —Publications: Related scientific papers
• —Instruments: Earth observation instruments
• —Platforms: Satellite and other observation platforms
• —Science Keywords: NASA Earth Science taxonomy

Training Process

• —Optimization: Adam optimizer
• —Loss Function: Contrastive loss for semantic alignment
• —Training Strategy:
• —Initial node embedding generation
• —Message passing through graph structure
• —Contrastive learning with NASA-SMD-IBM embeddings

Intended Use

Designed for: research, data discovery, and semantic search in Earth science Not intended for: safety‑critical systems or unrelated domains without fine‑tuning

Strengths

1. 1.Semantic Understanding:
2. 2.Strong performance in finding semantically related content
3. 3.Effective cross-modal relationships between text and graph structure

1. 1.Domain Specificity:
2. 2.Specialized for Earth science terminology
3. 3.Understands relationships between instruments, platforms, and datasets

1. 1.Multi-modal Integration:
2. 2.Combines text-based and graph-based features
3. 3.Preserves domain-specific relationships

Limitations

1. 1.Data Coverage:
2. 2.Performance depends on training data coverage
3. 3.May have gaps in newer or less documented areas

1. 1.Computational Requirements:
2. 2.Requires significant memory for full graph processing
3. 3.Graph operations can be computationally intensive

1. 1.Domain Constraints:
2. 2.Optimized for Earth science domain
3. 3.May not generalize well to other domains

Usage Guide

Installation Requirements

pip install torch torch-geometric transformers huggingface-hub

Basic Usage

from transformers import AutoTokenizer, AutoModel
import torch
from gnn_model import EOSDIS_GNN

# Load models
tokenizer = AutoTokenizer.from_pretrained("nasa-impact/nasa-smd-ibm-st-v2")
text_model = AutoModel.from_pretrained("nasa-impact/nasa-smd-ibm-st-v2")
gnn_model = EOSDIS_GNN.from_pretrained("your-username/eosdis-gnn")

# Process query
def get_embedding(text):
    inputs = tokenizer(text, return_tensors="pt", max_length=512, 
                      truncation=True, padding=True)
    with torch.no_grad():
        outputs = text_model(**inputs)
        return outputs.last_hidden_state[:, 0, :]

Semantic Search Example

from semantic_search import SemanticSearch

# Initialize searcher
searcher = SemanticSearch()

# Perform search
results = searcher.search(
    query="atmospheric carbon dioxide measurements",
    top_k=5,
    node_type="Dataset"  # Optional: filter by node type
)

Evaluation Metrics

Performance

Evaluation Notes:

• —Dataset: held‑out portion of NASA EOSDIS Knowledge Graph
• —Search task: queries derived from publication abstracts
• —Link prediction: 80/10/10 train/val/test splits
• —Numbers from offline evaluation; may vary on different graph snapshots

Version Control

• —Model versions tracked on Hugging Face Hub
• —Regular updates for improved performance

Citation

@misc{armin_mehrabian_2025,
	author       = { Armin Mehrabian },
	title        = { nasa-eosdis-heterogeneous-gnn (Revision 7e71e62) },
	year         = 2025,
	url          = { https://huggingface.co/arminmehrabian/nasa-eosdis-heterogeneous-gnn },
	doi          = { 10.57967/hf/6071 },
	publisher    = { Hugging Face }
}

Contact Information

• —Maintainer: Armin Mehrabian
• —Email: armin.mehrabian@nasa.gov
• —Organization: NASA