Dynamic topology and percolation criticality in higher-order activity–vulnerability driven networks

The study of temporal higher-order networks is crucial for forecasting phenomena such as information spread and systemic resilience. However, previous dominant models like the higher-order activity-driven framework capture connection formation but overlook the crucial role of dissolution in network dynamics. This omission biases predictions of network evolution and stability. In this article, we introduce the higher-order activity-vulnerability driven (HOAVD) networks, a novel framework that simultaneously captures hyperedge formation and dissolution by activity and vulnerability of nodes. We show that the competition between these two attributes induces a gradual dynamic phase transition at a characteristic timescale, separating the dynamics into a short-timescale, activity-dominated regime and a long-timescale, balanced regime. Meanwhile, we obtain analytical expressions of topological property of HOAVD networks. Moreover, we derive an critical balance condition for system-wide percolation in the balanced regime. This condition reveals that the connectivity is sensitively constrained by the statistical interplay between the distributions of activity and vulnerability, a phenomenon representing a fundamental mechanism that was invisible to previous models. Our analytical results, which also provide insights into the time-dependent topology, are supported by numerical simulations of real data. Therefore, the HOAVD framework offers a more complete and physically grounded foundation for predicting and controlling dynamics in social, biological, and technological systems.

Authors

Institutions

Publication Details

Journal
Chaos An Interdisciplinary Journal of Nonlinear Science
Published
2025-12-01
DOI
https://doi.org/10.1063/5.0308167
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Dynamic topology and percolation criticality in higher-order activity–vulnerability driven networks

Zihao Song, Xiaodong Zhang
Chaos An Interdisciplinary Journal of Nonlinear Science
Complex Network Analysis Techniques
article

Dynamic topology and percolation criticality in higher-order activity–vulnerability driven networks

Zihao Song, Xiaodong Zhang
article en

Abstract

The study of temporal higher-order networks is crucial for forecasting phenomena such as information spread and systemic resilience. However, previous dominant models like the higher-order activity-driven framework capture connection formation but overlook the crucial role of dissolution in network dynamics. This omission biases predictions of network evolution and stability. In this article, we introduce the higher-order activity-vulnerability driven (HOAVD) networks, a novel framework that simultaneously captures hyperedge formation and dissolution by activity and vulnerability of nodes. We show that the competition between these two attributes induces a gradual dynamic phase transition at a characteristic timescale, separating the dynamics into a short-timescale, activity-dominated regime and a long-timescale, balanced regime. Meanwhile, we obtain analytical expressions of topological property of HOAVD networks. Moreover, we derive an critical balance condition for system-wide percolation in the balanced regime. This condition reveals that the connectivity is sensitively constrained by the statistical interplay between the distributions of activity and vulnerability, a phenomenon representing a fundamental mechanism that was invisible to previous models. Our analytical results, which also provide insights into the time-dependent topology, are supported by numerical simulations of real data. Therefore, the HOAVD framework offers a more complete and physically grounded foundation for predicting and controlling dynamics in social, biological, and technological systems.

Chaos An Interdisciplinary Journal of Nonlinear ScienceVol. 35(12)
Shanghai Jiao Tong University (CN)
Openalex Percentile: Top 32%
Complex Network Analysis Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.