# phanein/deepwalk

DeepWalk - Deep Learning for Graphs

Repository: https://github.com/phanein/deepwalk
Canonical: https://ross.abutalabs.com/products/deepwalk
Homepage: http://www.perozzi.net/projects/deepwalk/
Language: Python
License: NOASSERTION
License Family: other
Last push: 2023-06-14T23:22:41+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": 4393, "days_push": 1176, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2759, forks 812 (observed 2026-08-28T04:07:18.374198+00:00)

## What it is
DeepWalk is a Python command-line tool that learns low-dimensional vector representations of vertices in graphs using short random walks and skipgram training. It takes graphs as adjacency lists, edge lists, or Matlab adjacency matrices and outputs node embeddings suitable for downstream machine learning tasks like multi-label node classification.

## Use cases
- generate node embeddings for a graph
- learn graph representations with random walks
- embed social network nodes for classification
- convert a graph into feature vectors for machine learning
- run deepwalk on an adjacency list or edge list
- evaluate graph embeddings on multi-label node classification

## When to choose
- you need classic DeepWalk node embeddings for graph mining or research
- you want a simple CLI that turns graph files into embedding vectors
- you are reproducing the DeepWalk paper's results on datasets like BlogCatalog
- you need unsupervised representations for nodes in a social or information network

## When to avoid
- you need modern graph neural network methods like node2vec, GCN, or GraphSAGE
- you want an actively developed library with recent features and support
- you need a Python API rather than a command-line tool
- your graphs are so large they require distributed or GPU-accelerated training

## Facets
- artifact type: cli-tool
- maturity: maintenance
- function: machine-learning, cli, data-science
- domain: machine-learning, graph-processing, data-science
- platform: python, cli, windows
- tags: graph-embeddings, network-embeddings, random-walks, node-classification, word2vec, skipgram, social-networks, unsupervised-learning, algorithms, linux, macos

## Member repositories
- phanein/deepwalk (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:18.374198+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 2026-08-30T08:18:47.360211+00:00, confidence not recorded.
  - readme: https://github.com/phanein/deepwalk (fetched 2026-08-28T04:07:18.374198+00:00, sha f5913bd0c5d8)
  - registry_pypi: https://pypi.org/pypi/deepwalk/json (fetched 2026-08-29T09:57:23.408160+00:00, sha 01fc5d738f29)
- Data as of 2026-08-30T08:39:29.467469+00:00.