# Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs

arXiv: https://arxiv.org/abs/2112.06567 · HTML: https://mlsanity.com/paper/2112.06567/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 60 references that are in the map.
- First posted on arXiv 13 Dec 2021.

## Builds on (most cited first, 17 of 17)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Knowledge Graph Embedding for Link Prediction: A Comparative Analysis](https://mlsanity.com/paper/2002.00819/) (2020)
- [PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings](https://mlsanity.com/paper/2007.14175/) (2020)
- [Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework](https://mlsanity.com/paper/2006.13365/) (2020)
- [A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective](https://mlsanity.com/paper/2102.10062/) (2021)
- [OpenBioLink: A benchmarking framework for large-scale biomedical link prediction](https://mlsanity.com/paper/1912.04616/) (2019)
- [Unbiased Graph Embedding with Biased Graph Observations](https://mlsanity.com/paper/2110.13957/) (2021)
- [Utilising Graph Machine Learning within Drug Discovery and Development](https://mlsanity.com/paper/2012.05716/) (2020)
- [Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphs](https://mlsanity.com/paper/2103.10367/) (2021)
- [Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery](https://mlsanity.com/paper/2105.10488/) (2021)
- [Adversarial Learning for Debiasing Knowledge Graph Embeddings](https://mlsanity.com/paper/2006.16309/) (2020)
- [Residual2Vec: Debiasing graph embedding with random graphs](https://mlsanity.com/paper/2110.07654/) (2021)

## Built on by (most cited first, 11 of 11)

- [BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge Graphs](https://mlsanity.com/paper/2306.03606/) (2023)
- [Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks](https://mlsanity.com/paper/2404.03139/) (2024)
- [TWIG: Towards pre-hoc Hyperparameter Optimisation and Cross-Graph Generalisation via Simulated KGE Models](https://mlsanity.com/paper/2402.06097/) (2024)
- [The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models](https://mlsanity.com/paper/2409.04103/) (2024)
- [A Survey on Knowledge Graph Structure and Knowledge Graph Embeddings](https://mlsanity.com/paper/2412.10092/) (2024)
- [Biological Factor Regulatory Neural Network](https://mlsanity.com/paper/2304.04982/) (2023)
- [GATher: Graph Attention Based Predictions of Gene-Disease Links](https://mlsanity.com/paper/2409.16327/) (2024)
- [Disentangling Node Attributes from Graph Topology for Improved Generalizability in Link Prediction](https://mlsanity.com/paper/2307.08877/) (2023)
- [Veni, Vidi, Vici: Solving the Myriad of Challenges before Knowledge Graph Learning](https://mlsanity.com/paper/2402.06098/) (2024)
- [Extending TWIG: Zero-Shot Predictive Hyperparameter Selection for KGEs based on Graph Structure](https://mlsanity.com/paper/2412.14801/) (2024)
- [Generalized Rank-based Evaluation for Knowledge Graph Completion: Perspectives, Framework, and Analyses](https://mlsanity.com/paper/2606.08921/) (2026)

## Abstract (arXiv)

Adoption of recently developed methods from machine learning has given rise to creation of drug-discovery knowledge graphs (KG) that utilize the interconnected nature of the domain. Graph-based modelling of the data, combined with KG embedding (KGE) methods, are promising as they provide a more intuitive representation and are suitable for inference tasks such as predicting missing links. One common application is to produce ranked lists of genes for a given disease, where the rank is based on the perceived likelihood of association between the gene and the disease. It is thus critical that these predictions are not only pertinent but also biologically meaningful. However, KGs can be biased either directly due to the underlying data sources that are integrated or due to modeling choices in the construction of the graph, one consequence of which is that certain entities can get topologically overrepresented. We demonstrate the effect of these inherent structural imbalances, resulting in densely-connected entities being highly ranked no matter the context. We provide support for this observation across different datasets, models as well as predictive tasks. Further, we present various graph perturbation experiments which yield more support to the observation that KGE models can be more influenced by the frequency of entities rather than any biological information encoded within the relations. Our results highlight the importance of data modeling choices, and emphasizes the need for practitioners to be mindful of these issues when interpreting model outputs and during KG composition.