Skip to content
Preprint

Giant strongly biconnected components of directed networks: a generating function approach

Aug 2026 · 0 citations · 46 references
Physics Biology

TL;DR

It is shown that the giant SBC emerges at the same threshold as the giant SCC but grows more slowly due to stricter connectivity requirements, which provides insight into the interplay between connectivity, redundancy, and robustness in complex directed systems.

Abstract

Strongly connected components (SCCs) characterize modular structure in directed networks but are fragile to single node failures. We study strongly biconnected components (SBCs), which are the set of nodes in which every node pair remains mutually reachable after the removal of any single node, as a more robust notion of connectivity. Using a generating function formalism, we derive the size of the giant SBC and analyze its percolation behavior under random node and link removal. We show that the giant SBC emerges at the same threshold as the giant SCC but grows more slowly due to stricter connectivity requirements. We also applied our theoretical framework to real-world biological networks including gene regulatory networks and neural connectome. Our framework provides insight into the interplay between connectivity, redundancy, and robustness in complex directed systems.

View source

Similar papers

Preprint Aug 2026

Criticality and universality in network dismantling

The proposed percolation process displays a universal phase transition, characterized by the abrupt and simultaneous disappearance of both the giant connected component and the largest 2-core, across networks with markedly different degree distributions, indicating that the physics of network dismantling is insensitive...

L. Cirigliano, Claudio Castellano, Minsuk Kim et al. · 0 citations
Open access Sep 2026

Modularity-based approach identifies small sets of control nodes in synthetic and biological Boolean networks

This study investigates the problem of controlling the dynamics of biological systems to achieve desired outcomes (attractors). Specifically, we describe biological systems with Boolean models, which represent the system as a network, characterize system components (nodes) with binary states, and use discrete functions...

F. Nasrollahi, E. Newby, Réka Albert · 1 citation
Preprint Aug 2026

Resilience Beyond Pairwise Networks

We derive a one-dimensional reduction for nonlinear dynamics on simplicial complexes containing both pairwise and triangular (higher-order) interactions. The effective state is defined using a mixed weight determined by the pairwise and triangular degrees of each node. The resulting reduced equation retains two structu...

Amit Tiwari, C. Hens, Prosenjit Kundu · 0 citations
Preprint Aug 2026

Reducing Boolean Networks via Analysis of Dynamic Network Subgraph Behavior

Boolean networks provide a compact framework for modeling regulatory systems, yet their rapidly expanding state spaces make systematic dynamical analysis challenging. Here, we systematically enumerate all non-isomorphic two-node signed regulatory subgraphs with their admissible Boolean update rules and exhaustively cha...

Soodabeh Zakeri, Mohieddin Jafari · 0 citations
Open access Aug 2026

Counting subgraphs in multiplex networks

The MPCount implementation builds on the FaSE algorithm, extending it to accommodate multiplex networks by adapting its efficient enumeration and isomorphism identification process to address the introduced layers, making it an available practical tool for counting subgraphs in multiplex networks.

A. Meira, P. Ribeiro · 0 citations
Preprint Aug 2026

Ollivier's Ricci Curvature on Complex-weighted Graphs

This work introduces a principled extension of Ollivier's Ricci curvature to complex-weighted graphs, which encompasses directed graphs as a special case and establishes fundamental theoretical properties of this new notion, including relations to the magnetic Laplacian and combinatorial upper and lower bounds that rel...

Yu Tian, Eleanor P. Wiesler, Melanie Weber · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.