# divelab/DIG

A library for graph deep learning research

Repository: https://github.com/divelab/DIG
Canonical: https://ross.abutalabs.com/products/divelab-dig
Homepage: https://diveintographs.readthedocs.io/
Language: Python
License: GPL-3.0
License Family: copyleft
Topics: deep-learning, graph-neural-network, graph-generation, explainable-ml, self-supervised-learning, 3d-graph
Last push: 2024-07-15T07:18:56+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2133, "days_push": 779, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2007, forks 289 (observed 2026-08-28T04:06:04.769026+00:00)

## What it is
DIG (Dive into Graphs) is a Python library built on PyTorch Geometric that provides a unified testbed for advanced graph deep learning research, covering graph generation, self-supervised learning, explainability, 3D graphs, and out-of-distribution generalization. It complements lower-level libraries like PyG and DGL by offering higher-level, research-oriented implementations and benchmarks.

## Use cases
- research graph generation models
- self-supervised learning on graphs
- explainability methods for graph neural networks
- 3D graph deep learning experiments
- graph out-of-distribution generalization benchmarks
- develop new graph deep learning methods on a unified testbed

## When to choose
- you need higher-level graph research tasks beyond what PyG or DGL provide
- you want reproducible benchmarks for graph generation, explainability, or self-supervised learning
- you are doing academic research in graph deep learning

## When to avoid
- you only need basic GNN layers and data loading - use PyG or DGL directly
- you need a production system rather than a research testbed
- you require frequent updates or long-term support - development activity has slowed

## Facets
- artifact type: library
- maturity: maintenance
- function: deep-learning, machine-learning
- domain: deep-learning, machine-learning
- platform: python
- tags: graph-neural-networks, graph-generation, explainability, self-supervised-learning, 3d-graphs, research, pytorch-geometric, algorithms

## Member repositories
- divelab/DIG (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:04.769026+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:01:24.815246+00:00, confidence not recorded.
  - readme: https://github.com/divelab/DIG (fetched 2026-08-28T04:06:04.769026+00:00, sha e06c1ac0f60d)
- Data as of 2026-08-30T08:39:29.467469+00:00.