Documentation  |  Quickstart  |  Results  |  Roadmap

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Mission

One place to find, run and compare entity-alignment models on DBP15K.

Entity Alignment (EA) research is scattered across dozens of repositories, each with its own data format, training loop and evaluation quirks. EntityAlignment-Nexus brings nine landmark methods together under a single, readable codebase with a shared data layer, shared metrics and a shared trainer pattern, so you can study a method, reproduce its numbers, and compare it to the others without re-learning a new project every time.

Every model ships twice:

• as an installable package (code/) driven by one YAML config per model, and
• as a self-contained notebook (Notebook/) that re-implements the whole engine inline, documented cell by cell, so you can read a method top to bottom in one file.

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Highlights

Unified & faithful 9 models, 1 data loader, 1 metrics module, 1 trainer pattern DBP15K zh_en / ja_en / fr_en, the standard 30% seed split CSLS, MRR, Hit@1/5/10 reported the same way for everyone

Reproducible & transparent Timestamped run dirs: config snapshot, logs, CSV, checkpoints, curves Paper-level (or above) on several models, gaps documented honestly "Debugging lessons" notes explaining what actually made each model work

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The model collection

flowchart LR
    subgraph S["Structural"]
        NAEA["NAEA<br/>IJCAI'19"]
        BootEA["BootEA<br/>IJCAI'18"]
        AliNet["AliNet<br/>AAAI'20"]
        GCN["GCN-Align<br/>EMNLP'18"]
    end
    subgraph R["Relation-aware GNN"]
        KECG["KECG<br/>EMNLP'19"]
        MRAEA["MRAEA<br/>WSDM'20"]
        RREA["RREA<br/>CIKM'20"]
    end
    subgraph A["Side information"]
        JAPE["JAPE<br/>attributes<br/>ISWC'17"]
        DGMC["DGMC<br/>entity names<br/>ICLR'20"]
    end
    DBP["DBP15K"] --> S & R & A
    classDef s fill:#1f6feb,stroke:#58a6ff,color:#fff;
    classDef r fill:#8957e5,stroke:#a371f7,color:#fff;
    classDef a fill:#bf4b8a,stroke:#f778ba,color:#fff;
    class NAEA,BootEA,AliNet,GCN s;
    class KECG,MRAEA,RREA r;
    class JAPE,DGMC a;

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Model	Venue	Family	One-line idea	Page
NAEA	IJCAI 2019	structural + attention	Neighbourhood-aware GAT over translation-consistent messages	docs
BootEA	IJCAI 2018	structural + bootstrap	AlignE TransE + alignment-by-swapping + editable MWGM self-training	docs
AliNet	AAAI 2020	gated multi-hop GNN	1-hop GCN + attentional 2-hop, fused by a learned gate	docs
KECG	EMNLP 2019	GNN + TransE	Shared diagonal GAT cross-graph + knowledge-embedding loss	docs
GCN-Align	EMNLP 2018	GCN	Functionality-weighted adjacency + shared 2-layer GCN (SE)	docs
JAPE	ISWC 2017	TransE + attributes	Merged-seed TransE fused with a TF-IDF attribute channel	docs
DGMC	ICLR 2020	name features + GNN	GloVe name features + sparse top-k neighbourhood consensus	docs
MRAEA	WSDM 2020	meta-relation GNN	Relation + inverse aware GAT + iterative mutual-NN bootstrap	docs
RREA	CIKM 2020	relational reflection	Householder reflection aggregation + turn-based CSLS bootstrap	docs

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Results

DBP15K zh_en, 30% seed. Bold = this repo matches or beats the paper. Full tables for ja_en / fr_en and training curves live in the results page.

Model	Hit@1 (paper)	Hit@1 (here)	Hit@10 (paper)	Hit@10 (here)	MRR (paper)	MRR (here)
NAEA	0.650	~0.62	0.867	~0.86	0.720	~0.70
BootEA	0.629	~0.56	0.847	~0.85	0.703	~0.66
AliNet	0.539	~0.53	0.826	~0.81	0.628	~0.63
KECG	0.477	~0.42	0.835	~0.73	0.598	~0.52
GCN-Align (SE)	0.384	~0.38	0.703	~0.68	-	~0.49
JAPE (SE+AE)	0.412	0.425	0.745	0.761	0.490	0.537
DGMC (names)	0.801	0.767	0.875	0.840	-	-
MRAEA (base)	0.638	0.659	0.882	0.898	0.729	0.746
MRAEA (+iter)	0.757	0.746	0.930	0.930	0.827	0.814
RREA (basic)	0.715	0.712	0.929	0.934	0.794	0.793
RREA (semi)	0.801	0.805	0.948	0.950	0.857	0.859

RREA (semi-supervised) is the current top performer in this repo. DGMC, which uses entity names, even beats the paper on fr_en (Hit@1 0.939 vs 0.933).

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Quickstart

# 1. install the package (editable)
cd code
pip install -e .

# 2. train any model from its YAML config
python -m src.main --config ../configs/rrea_dbp15k.yaml          # RREA (semi-supervised)
python -m src.main --config ../configs/jape_dbp15k.yaml          # JAPE (SE + attributes)
python -m src.main --config ../configs/dgmc_dbp15k.yaml --lang zh_en   # DGMC (entity names)

# 3. override on the fly
python -m src.main --config ../configs/mraea_dbp15k.yaml --lang fr_en --epochs 2000

Each run writes a timestamped folder (config snapshot, training.txt, loss.csv, metrics.csv, dark-theme curves, checkpoints, embeddings).

Prefer reading over running? Open any notebook in Notebook/ - each one re-implements the full pipeline inline, documented cell by cell.

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Repository layout

EntityAlignment-Nexus/
├── code/                  # installable package (pip install -e .)
│   └── src/
│       ├── data.py        # DBP15K loading + graph builders + samplers
│       ├── trainer.py     # all 9 trainers (shared pattern)
│       ├── main.py        # CLI entry point (YAML-driven)
│       ├── models/        # one file per model + its losses
│       └── utils/         # config, logging, metrics (MRR/Hit@k/CSLS), plotting
├── configs/               # one YAML per model
├── Notebook/              # 9 self-contained, documented notebooks
└── docs/                  # MkDocs Material documentation site
#  (training runs are written to a git-ignored logs/ directory)

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Documentation

A full MkDocs Material site (dark + light theme, Mermaid diagrams, math, training curves) covers every model in depth:

pip install -r requirements-docs.txt
mkdocs serve        # live preview at http://127.0.0.1:8000

It is deployed automatically to GitHub Pages on every push to main (see .github/workflows/docs.yml).

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Roadmap

The repo is built to grow. Next up: transformer-based entity-alignment models (self-attention encoders, pre-trained language model initialisation, and large-model EA pipelines). See the roadmap for the shortlist and how to propose a model.

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Contributing

Contributions are very welcome - a new model, a stronger config, a ja_en/fr_en run, or a doc fix. The shared data.py / trainer.py / metrics.py make adding a model mostly a matter of writing models/<your_model>.py, a trainer and a YAML. See the about page.

Citation

@software{ea_dbp15k,
  title  = {EntityAlignment-Nexus: A Unified Framework of Entity Alignment Models on DBP15K},
  author = {Nadjib ZAHAF},
  year   = {2026},
  url    = {https://github.com/Z-Nadjib/EntityAlignment-Nexus}
}

License

Released under the MIT License. The original papers and the DBP15K benchmark remain the property of their respective authors.