# benedekrozemberczki/awesome-graph-classification

A collection of important graph embedding, classification and representation learning papers with implementations.

Repository: https://github.com/benedekrozemberczki/awesome-graph-classification
Canonical: https://ross.abutalabs.com/products/awesome-graph-classification
Language: Python
License: CC0-1.0
License Family: permissive
Topics: graph2vec, classification-algorithm, graph-kernels, weisfeiler-lehman, kernel-methods, deep-graph-kernels, netlsd, graph-attention-model, graph-attention-networks, structural-attention, attention-mechanism, graph-kernel, node2vec, graph-embedding, network-embedding, node-embedding, graph-representation-learning, deepwalk, graph-convolutional-networks, graph-classification
Last push: 2023-03-18T12:10:08+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": 2972, "days_push": 1264, "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 4800, forks 724 (observed 2026-08-28T04:09:00.168651+00:00)

## What it is
A curated awesome-list of graph classification, embedding, and representation learning papers organized into matrix factorization, spectral fingerprints, deep learning, and graph kernels, each with reference implementations. It serves as a research index rather than a runnable library.

## Use cases
- find papers on graph embedding with code
- learn graph classification methods
- compare graph kernel approaches
- find node2vec and deepwalk implementations
- survey graph representation learning literature
- find graph neural network papers with reference code

## When to choose
- researching graph classification or embedding literature
- looking for implementations of graph kernel or GNN papers
- building a reading list for graph representation learning

## When to avoid
- you need a production-ready graph ML library
- you want a maintained tool rather than a paper index
- you need benchmark datasets themselves (see the linked graph_datasets repo)

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, developer-tools
- domain: machine-learning, artificial-intelligence, tutorials, awesome-lists
- platform: python
- tags: awesome-list, graph-embedding, graph-classification, graph-kernels, node2vec, deep-learning, papers-with-implementations, representation-learning

## Member repositories
- benedekrozemberczki/awesome-graph-classification (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:00.168651+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 2026-08-29T18:18:37.581678+00:00, confidence not recorded.
  - readme: https://github.com/benedekrozemberczki/awesome-graph-classification (fetched 2026-08-28T04:09:00.168651+00:00, sha f5a6139ac2e9)
- Data as of 2026-08-30T08:39:29.467469+00:00.