Residual2Vec: Debiasing graph embedding with random graphs

Kojaku, Sadamori; Yoon, Jisung; Constantino, Isabel; Ahn, Yong‐Yeol

doi:10.48550/arxiv.2110.07654

Cited by 2 publications

(1 citation statement)

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“…The issue of non-uniform graph connectivity (typically in homogenous graphs) has begun to be studied in parallel by the field of Graph Neural Networks (GNN), where researchers have shown that models learn low-quality representations, thus making more incorrect predictions, for low-degree vertices [26,25,44]. This has also been explored in the context of homogenous graph representation learning [3] and for random walks [23,36].…”

Section: Previous Workmentioning

confidence: 99%

Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs

Bonner,

Kirik,

Engkvist

et al. 2021

Preprint

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Improving on the standard of care for a given disease is predicated on better treatments, which in turn relies on finding and developing new drugs. However, drug discovery is a complex, cross-disciplinary and costly process. Adoption of recently developed methods from machine learning has given rise to creation of drug discovery knowledge graphs which utilize the inherent interconnected nature of the domain. Graph-based modelling of the data, combined with knowledge graph embedding methods which aim to learn meaningful representations of biological entities, are promising as they provide a more intuitive representation of the domain and are suitable for inference tasks such as predicting missing links. In this context, one such example would be producing ranked lists of likely associated genes for a given disease, often referred to as target discovery. It is thus critical that these predictions are not only pertinent but also biologically meaningful.

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