Event Embedding of Protein Networks : Compositional Learning of Biological Function

• URL: http://arxiv.org/abs/2604.00911v1
• Date: Wed, 01 Apr 2026 13:53:35 GMT
• Title: Event Embedding of Protein Networks : Compositional Learning of Biological Function
• Abstract summary: We train 64-dimensional representations on random walks from the human STRING interactome.<n>We find that compositional structure substantially improves pathway coherence.<n>These results indicate that enforced compositionality specifically benefits relational and compositional reasoning tasks in biological networks.
• Score: 0.15229257192293197
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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