# Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations

arXiv: https://arxiv.org/abs/1906.05017 · HTML: https://mlsanity.com/paper/1906.05017/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2019.

## Built on by (most cited first, 30 of 50)

- [Graph Representation Learning in Biomedicine](https://mlsanity.com/paper/2104.04883/) (2021)
- [On the Robustness of Graph Neural Diffusion to Topology Perturbations](https://mlsanity.com/paper/2209.07754/) (2022)
- [Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach](https://mlsanity.com/paper/2310.06396/) (2023)
- [A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications](https://mlsanity.com/paper/2308.16375/) (2023)
- [EXGC: Bridging Efficiency and Explainability in Graph Condensation](https://mlsanity.com/paper/2402.05962/) (2024)
- [Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery](https://mlsanity.com/paper/2105.10488/) (2021)
- [AutoGEL: An Automated Graph Neural Network with Explicit Link Information](https://mlsanity.com/paper/2112.01064/) (2021)
- [Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND](https://mlsanity.com/paper/2404.17099/) (2024)
- [Learnable Hypergraph Laplacian for Hypergraph Learning](https://mlsanity.com/paper/2106.05701/) (2021)
- [Multi-task Joint Strategies of Self-supervised Representation Learning on Biomedical Networks for Drug Discovery](https://mlsanity.com/paper/2201.04437/) (2022)
- [Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks](https://mlsanity.com/paper/2305.18965/) (2023)
- [Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction](https://mlsanity.com/paper/2306.05257/) (2023)
- [Cleora: A Simple, Strong and Scalable Graph Embedding Scheme](https://mlsanity.com/paper/2102.02302/) (2021)
- [Task-Equivariant Graph Few-shot Learning](https://mlsanity.com/paper/2305.18758/) (2023)
- [Is Solving Graph Neural Tangent Kernel Equivalent to Training Graph Neural Network?](https://mlsanity.com/paper/2309.07452/) (2023)
- [DINE: Dimensional Interpretability of Node Embeddings](https://mlsanity.com/paper/2310.01162/) (2023)
- [DUPLEX: Dual GAT for Complex Embedding of Directed Graphs](https://mlsanity.com/paper/2406.05391/) (2024)
- [Large Language Models Meet Text-Attributed Graphs: A Survey of Integration Frameworks and Applications](https://mlsanity.com/paper/2510.21131/) (2025)
- [Learning to Model the Relationship Between Brain Structural and Functional Connectomes](https://mlsanity.com/paper/2112.09906/) (2021)
- [Robust Graph Neural Networks using Weighted Graph Laplacian](https://mlsanity.com/paper/2208.01853/) (2022)
- [Self-explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction](https://mlsanity.com/paper/2309.06584/) (2023)
- [Graph Neural Networks for Protein-Protein Interactions -- A Short Survey](https://mlsanity.com/paper/2404.10450/) (2024)
- [Free Energy Node Embedding via Generalized Skip-gram with Negative Sampling](https://mlsanity.com/paper/2105.09182/) (2021)
- [Accurately Modeling Biased Random Walks on Weighted Graphs Using textitNode2vec+](https://mlsanity.com/paper/2109.08031/) (2021)
- [Fast Graph Neural Tangent Kernel via Kronecker Sketching](https://mlsanity.com/paper/2112.02446/) (2021)
- [A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods](https://mlsanity.com/paper/2112.10372/) (2021)
- [HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks](https://mlsanity.com/paper/2308.14340/) (2023)
- [Drug-Target Interaction/Affinity Prediction: Deep Learning Models and Advances Review](https://mlsanity.com/paper/2502.15346/) (2025)
- [Effective Clustering for Large Multi-Relational Graphs](https://mlsanity.com/paper/2508.17388/) (2025)
- [HypoEvolve: Genetic Algorithms Enable Multi-Agent LLMs to Discover Scientific Hypotheses](https://mlsanity.com/paper/2609.15938/) (2026)

## Abstract (arXiv)

Graph embedding learning that aims to automatically learn low-dimensional node representations, has drawn increasing attention in recent years. To date, most recent graph embedding methods are evaluated on social and information networks and are not comprehensively studied on biomedical networks under systematic experiments and analyses. On the other hand, for a variety of biomedical network analysis tasks, traditional techniques such as matrix factorization (which can be seen as a type of graph embedding methods) have shown promising results, and hence there is a need to systematically evaluate the more recent graph embedding methods (e.g. random walk-based and neural network-based) in terms of their usability and potential to further the state-of-the-art. We select 11 representative graph embedding methods and conduct a systematic comparison on 3 important biomedical link prediction tasks: drug-disease association (DDA) prediction, drug-drug interaction (DDI) prediction, protein-protein interaction (PPI) prediction; and 2 node classification tasks: medical term semantic type classification, protein function prediction. Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis. Compared with three state-of-the-art methods for DDAs, DDIs and protein function predictions, the recent graph embedding methods achieve competitive performance without using any biological features and the learned embeddings can be treated as complementary representations for the biological features. By summarizing the experimental results, we provide general guidelines for properly selecting graph embedding methods and setting their hyper-parameters for different biomedical tasks.