# palash1992/GEM

Repository: https://github.com/palash1992/GEM
Canonical: https://ross.abutalabs.com/products/gem
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: graph-embedding, graph-embeddings, network-embedding, network-visualization, link-prediction
Last push: 2023-11-05T22:04:24+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3404, "days_push": 1032, "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 1288, forks 357 (observed 2026-08-28T04:04:15.109730+00:00)

## What it is
GEM is a Python library offering a unified framework for static graph embedding algorithms, including node2vec, HOPE, SDNE, Graph Factorization, Laplacian Eigenmaps, and Locally Linear Embedding. It ships evaluation utilities for link prediction, graph reconstruction, node classification, and visualization, with a C++/Boost backend for faster execution.

## Use cases
- learn low-dimensional node embeddings for a network
- compare graph embedding algorithms under a common framework
- predict missing links in a graph
- visualize a network in 2D via its embeddings
- classify nodes using embedding features
- reconstruct graph edges from learned embeddings

## When to choose
- You want to run and benchmark multiple classic static graph embedding methods through one consistent Python API
- You need built-in evaluation tasks such as link prediction, node classification, and graph reconstruction to judge embedding quality
- You are doing research or a survey on graph/network embedding techniques and want an extensible framework for adding new methods

## When to avoid
- You need dynamic or evolving graph embeddings (use the companion DynamicGEM library instead)
- You need a large-scale, GPU-accelerated or production-grade graph learning stack (PyTorch Geometric, DGL)
- You require knowledge-graph embeddings with entity/relation features (TransE-style) or attributed graph methods
- You need a library with frequent recent updates and long-term maintenance guarantees

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, benchmarking, data-visualization
- domain: machine-learning, data-science
- platform: python, cross-platform
- tags: graph-embedding, network-embedding, node2vec, link-prediction, node-classification, graph-reconstruction, graph-representation-learning, static-graphs, research-library, algorithms

## Member repositories
- palash1992/GEM (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:15.109730+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 2026-08-30T04:55:49.294513+00:00, confidence not recorded.
  - readme: https://github.com/palash1992/GEM (fetched 2026-08-28T04:04:15.109730+00:00, sha e1a924561f2e)
- Data as of 2026-08-30T08:39:29.467469+00:00.