[2604.00911] Event Embedding of Protein Networks : Compositional Learning of Biological Function

Computer Science > Machine Learning arXiv:2604.00911 (cs) [Submitted on 1 Apr 2026] Title:Event Embedding of Protein Networks : Compositional Learning of Biological Function Authors:Antonin Sulc View a PDF of the paper titled Event Embedding of Protein Networks : Compositional Learning of Biological Function, by Antonin Sulc View PDF HTML (experimental) Abstract:In this work, we study whether enforcing strict compositional structure in sequence embeddings yields meaningful geometric organization when applied to protein-protein interaction networks. Using Event2Vec, an additive sequence embedding model, we train 64-dimensional representations on random walks from the human STRING interactome, and compare against a DeepWalk baseline based on Word2Vec, trained on the same walks. We find that compositional structure substantially improves pathway coherence (30.2$\times$ vs 2.9$\times$ above random), functional analogy accuracy (mean similarity 0.966 vs 0.650), and hierarchical pathway organization, while geometric properties such as norm--degree anticorrelation are shared with or exceeded by the non-compositional baseline. These results indicate that enforced compositionality specifically benefits relational and compositional reasoning tasks in biological networks. Comments: Subjects: Machine Learning (cs.LG) Cite as: arXiv:2604.00911 [cs.LG]   (or arXiv:2604.00911v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2604.00911 Focus to learn more arXiv-issued DOI via ...