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arXiv · 2605.11450

Scalable vertex guided filtrations identify structurally relevant genes in cancer networks

Edmara Viana·Rodrigo Henrique Ramos·Flávia Raquel Gonçalves Carneiro·Cynthia de Oliveira Lage Ferreira
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Abstract

Topological data analysis (TDA) has established itself as a useful tool for capturing multiscale structures in complex networks, such as connected components, cycles, and cavities. Although Vietoris-Rips (VR) filtering is widely used in network analysis, it tends to be computationally expensive, especially for large networks. This work explores vertex function-based (VFB) filtering based on network measures, applying persistent homology to identify relevant topological structures in cancer-associated protein networks, and compares its effectiveness with the VR approach. The results show that VFB reproduces the second-order structures (Betti-2) identified by VR, recovering previously reported essential genes. In addition, VFB detected new driver genes, confirmed in databases such as IntOGen and NCG, and allowed analysis of third-order structures (Betti-3) that was not feasible with VR. Thus, VFB represents a scalable alternative to VR, preserving biological interpretability and complementing classical network metrics.

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Edmara Viana, Rodrigo Henrique Ramos, Flávia Raquel Gonçalves Carneiro, Cynthia de Oliveira Lage Ferreira. 2026-05-12. Scalable vertex guided filtrations identify structurally relevant genes in cancer networks. https://arxiv.org/abs/2605.11450

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