Residual2Vec: Debiasing graph embedding using random graphs

Related tags

Text Data & NLP residual2vec
Overview

Unit Test & Deploy

Residual2Vec: Debiasing graph embedding using random graphs

This repository contains the code for

  • 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): https://arxiv.org/abs/2110.07654

  • 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}
}

Installation and Usage of residual2vec package

pip install residual2vec

The code and instruction for residual2vec sits in libs/residual2vec. See here.

Reproducing the results

We set up Snakemake workflow to reproduce our results. To this end, install snakemake and run

snakemake --cores <# of cores available> all

which will produce all figures for the link prediction and community detection benchmarks. The results for the case study are not generated due to the limitation by our data sharing aggreements.

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Comments
  • Adding single context window

    Adding single context window

    Update

    • Implemented a single window that extends leftward or rightward of a center node. This feature may be useful to embed directed networks. Available only for the pytorch version of the residual2vec

    How to specify the context window types

    The residual2vec_sgd takes a new argument context_window_type that specifies the type of context window. The default is double:

    context_window_type = 'left' # {'left', 'right', 'double'} 
rv.residual2vec_sgd(context_window_type=context_window_type)

    context_window_type="double" specifies the context window that extends both left and right of a focal node. context_window_type="left" or ="right" specifies that extends left or right, respectively.

    opened by skojaku 0
  • Remove clamp to prevent the gradient to be nan

    Remove clamp to prevent the gradient to be nan

    Update

    • Stop using .clamp in the computation of the sigmoid function. clamp has been used for the numerical stability of the sigmoid function. But later, I found that, when using clamp function, sometimes the gradient becomes nan, and this breaks the embedding. PyTorch community is aware of this problem and recommends using 'logsigmoid' function, instead. So, in this new version, the logsimoid function is used.
    • Add an experimental sampler ConditionalContextSampler. This sampler takes group memberships of nodes, and samples a random context from a group that a given context belongs to. May be useful to control the effect of group structure.
    opened by skojaku 0
  • residual2vec with a stochastic gradient descent algorithm for embedding large networks

    residual2vec with a stochastic gradient descent algorithm for embedding large networks

    Issue: The current implementation based on a matrix factorization is memory demanding especially for large networks. The memory consumption is marginal up to 1M but considerable for larger networks, which prevents me to use residua2vec for some of my projects. This update aims to address this issue by using the stochastic gradient descent algorithm, which updates the embedding incrementally using a small chunk of data that can be fitted into memory.

    opened by skojaku 0
  • Fix bugs that appear when simplifying code

    Fix bugs that appear when simplifying code

    This repo contains the simplified version of my code in yy/residual-node2vec. Because I have some time, I run each line of code for both implementations and compared. I found that a bug enters my code in the process of simplification. This patch fixes this bug and I checked that the results are qualitatively reproduced.

    opened by skojaku 0
Owner
SADAMORI KOJAKU
SADAMORI KOJAKU
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