residual2vec 0.0.2

residual2vec: debiasing graph embedding with random graphs

Python package for residual2vec graph embedding algorithm

residual2vec is an algorithm to embed networks to a vector space while controlling for various structural properties such as degree. If you use this package, please cite:

• S. Kojaku, J. Yoon, I. Constantino, and Y.-Y. Ahn, Residual2Vec: Debiasing graph embedding using random graphs. NerurIPS (2021). [link will be added when available]

• Preprint (arXiv): [link to arXiv]

• BibTex entry:

@inproceedings{kojaku2021neurips,
 title={Residual2Vec: Debiasing graph embedding using random graphs},
 author={Sadamori Kojaku and Jisung Yoon and Isabel Constantino and Yong-Yeol Ahn},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {},
 pages = {},
 publisher = {Curran Associates, Inc.},
 volume = {},
 year = {2021}
}

Install

pip install residual2vec

Requirements

This code is tested in Python 3.7 and 3.8, and has dependencies with the following packages:

- numpy==1.20.3
- scipy==1.7.1
- scikit-learn==1.0
- faiss-cpu==1.7.0

Example

import residual2vec as rv

model = rv.residual2vec(window_length = 10, group_membership = None)
model.fit(G)
emb = model.transform(dim = 64)
# or equivalently emb = model.fit(G).transform(dim = 64)

• G: adjacency matrix of the input graph. numpy.array or scipy.sparse.csr_matrix can be accepted.
• window_length: the length of context window.
• group_membership: an array of node labels. Used to debias the structural bias correlated with the node labels.
• dim: Dimension of the embedding
• emb: 2D numpy array of shape (N, dim), where N is the number of nodes. The ith row in the array (i.e., emb[i, :]) represents the embedding vector of the ith node in the given adjacency matrix G.