# thunlp/NRLPapers

Must-read papers on network representation learning (NRL) / network embedding (NE)

Repository: https://github.com/thunlp/NRLPapers
Canonical: https://ross.abutalabs.com/products/nrlpapers
Language: TeX
License Family: other
Topics: network-embedding, paper-list
Last push: 2020-08-03T10:53:46+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3396, "days_push": 2221, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2514, forks 634 (observed 2026-08-28T04:06:57.551946+00:00)

## What it is
A curated list of must-read papers on network representation learning (NRL) and network embedding (NE), organized by model type and application area. It is a reading resource maintained by THUNLP, companion to their OpenNE toolkit.

## Use cases
- find must-read papers on network embedding
- learn graph representation learning from scratch
- survey literature on node2vec and DeepWalk
- find papers on graph embedding for recommendation
- prepare a literature review on network representation learning

## When to choose
- starting research in network embedding or graph representation learning
- building a reading list for a course or thesis on graph learning
- looking for surveys and foundational NRL models

## When to avoid
- you need runnable code rather than a paper list
- you need up-to-date papers after 2020, as the list is no longer actively updated
- you need general graph neural network resources beyond NRL/NE

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, documentation
- domain: machine-learning, tutorials, awesome-lists
- platform: cross-platform
- tags: paper-list, network-embedding, graph-embedding, curated-list, research-papers, algorithms

## Member repositories
- thunlp/NRLPapers (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:57.551946+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 2026-08-30T02:26:14.187253+00:00, confidence not recorded.
  - readme: https://github.com/thunlp/NRLPapers (fetched 2026-08-28T04:06:57.551946+00:00, sha 2ae2fd0ab737)
- Data as of 2026-08-30T08:39:29.467469+00:00.