arXiv subjects

Yajing Hao

Publications and source records attributed to Yajing Hao.

6 recordsLinked to original sources

arXiv2026

Immunization on Temporal Higher-Order Networks

Network immunization is a powerful tool for controlling contagion processes ranging from infectious diseases to misinformation diffusion. While prior works have focused on pairwise or static networks, immunization dynamics in temporal higher-order networks remain poorly understood. Here, we introduce immunization strategies and develop a theoretical framework tailored for such temporal systems. Firstly, we reveal bistability and discontinuous transitions in prevalence as the immunization fraction varies. This implies that immunization effectiveness depends on the initial prevalence, marking a fundamental departure from pairwise networks. Building on this prevalence-dependent behavior, we propose the High Infection Contribution (HIC) strategy, demonstrating its superior performance over all evaluated heuristic strategies. Furthermore, we introduce egocentric strategies by leveraging solely local observations. Notably, the optimal egocentric strategy shifts with the contagion prevalence. Our work advances the understanding of network immunization, paving the way for effective contagion control in temporal higher-order networks.

physics.soc-ph↗

arXiv2026

Identifying vulnerable nodes for hypergraph dismantling via higher-order competition dynamics

Network dismantling aims to identify a node removal sequence that can rapidly destroy network connectivity, which is an important problem for understanding the structural fragility of complex systems and designing intervention strategies. Existing studies mainly focus on pairwise networks or assume weak-deletion rules where node removal only causes hyperedges to shrink in higher-order networks. However, in many real higher-order systems, the failure of one participant may cause the entire group interaction to fail, i.e., the strong-deletion mechanism. Such a mechanism cannot be fully captured by projected networks or methods based on weak-deletion rules. To address this challenge, we propose hyper-Vulnerability-weighted Dominance rank (hyper-VDrank), a higher-order centrality method for hypergraph dismantling under strong deletion. Hyper-VDrank constructs a higher-order competition dynamics mediated by hyperedge-induced environments, where a node does not compete only with individual neighbors but responds to the collective pressure formed by other nodes in the same hyperedge. It further introduces a hyperedge vulnerability weight based on redundancy and size effects to capture the vulnerable structures, facilitating the distinction of critical nodes. Experiments show that hyper-VDrank reduces the largest connected component more rapidly, collapses the hypergraph earlier, and produces greater structural fragmentation than classical and recent methods. On 14 real-world hypergraphs, hyper-VDrank improves dismantling efficiency by 23.65% and reduces the collapse threshold by 27.63% on average compared with the baselines. In summary, hyper-VDrank offers an effective hypergraph dismantling tool and a new perspective on identifying vulnerable structures in higher-order complex systems.

physics.soc-ph↗

arXiv2026

Emergent dilemma and periodic oscillation in the nonlinear interplay between epidemic and behavior

Human behaviors, particularly non-pharmaceutical interventions (NPIs), are dynamically coupled with epidemic spreading. While prior studies mainly assume a linear interplay, real-world behavioral evolution is driven by nonlinear responses and social influence. Here, we incorporate these multifaceted mechanisms into a co-evolutionary model and analytically derive the critical thresholds. Notably, as the infection rate grows, NPI compliance initially rises but then abruptly drops to zero. This paradoxical decline indicates an emergent social dilemma: at high infection rates, abandoning NPIs is individually optimal but detrimentally triggers an explosive surge in epidemic prevalence. We further show that socially induced overestimation of the infection rate can counterintuitively prompt individuals to abandon NPIs. Moreover, the interplay with social influence induces periodic oscillations, reflecting a tragic cycle of recurrent epidemic waves. Furthermore, we validate the robustness of this NPI-abandonment dilemma in networked population. Our work illustrates rich emergent phenomena in the co-evolution of epidemic and behavior, challenging traditional views on this coupled dynamics.

physics.soc-ph↗

arXiv2025

A unified framework for identifying influential nodes in hypergraphs

Identifying influential nodes plays a pivotal role in understanding, controlling, and optimizing the behavior of complex systems, ranging from social to biological and technological domains. Yet most centrality-based approaches rely on pairwise topology and are purely structural, neglecting the higher-order interactions and the coupling between structure and dynamics. Consequently, the practical effectiveness of existing approaches remains uncertain when applied to complex spreading processes. To bridge this gap, we propose a unified framework, Initial Propagation Score (IPS), to directly embed propagation dynamics into influence assessment on higher-order networks. We analytically derive mechanism-aware influence measures by relating the early-stage dynamics and local topological characteristics to long-term outbreak sizes, and such explicit physical context endows IPS with robustness, transferability, and interpretability. Extensive experiments across multiple dynamics and more than 20 real-world hypergraphs show that IPS consistently outperforms other leading baseline centralities. Furthermore, IPS estimates node influence with only local neighborhood information, yielding computational efficiency and scalability to large-scale networks. This work underscores the necessity of considering dynamics for reliable identification of influential nodes and provides a concise principled basis for optimizing interventions in epidemiology, information diffusion, and collective intelligence.

physics.soc-ph↗

arXiv2024

Indirect reciprocity in the public goods game with collective reputations

Indirect reciprocity unveils how social cooperation is founded upon moral systems. Within the frame of dyadic games based on individual reputations, the "leading-eight" strategies distinguish themselves in promoting and sustaining cooperation. However, in the real-world societies, there are widespread interactions at the group level, where individuals need to make a singular action choice when facing multiple individuals with different reputations. Here, through introducing the assessment of collective reputations, we develop a framework that embeds group-level reputation structure into public goods game to study the evolution of group-level indirect reciprocity. We show that changing the criteria of group assessment destabilize the reputation dynamics of leading-eight strategies. In a particular range of social assessment criteria, all leading-eight strategies can break the social dilemma in public goods games and sustain cooperation. Specifically, there exists an optimal, moderately set assessment criterion that is most conducive to promoting cooperation. Moreover, in the evolution of assessment criteria, the preference of the leading-eight strategies for social strictness is inversely correlated with the payoff level. Our work reveals the impact of social strictness on prosocial behavior, highlighting the importance of group-level interactions in the analysis of evolutionary games and complex social dynamics.

physics.soc-ph↗

arXiv2022

Probabilistic activity driven model of temporal simplicial networks and its application on higher-order dynamics

Network modeling characterizes the underlying principles of structural properties and is of vital significance for simulating dynamical processes in real world. However, bridging structure and dynamics is always challenging due to the multiple complexities in real systems. Here, through introducing the individual's activity rate and the possibility of group interaction, we propose a probabilistic activity driven (PAD) model that could generate temporal higher-order networks with both power-law and high-clustering characteristics, which successfully links the two most critical structural features and a basic dynamical pattern in extensive complex systems. Surprisingly, the power-law exponents and the clustering coefficients of the aggregated PAD network could be tuned in a wide range by altering a set of model parameters. We further provide an approximation algorithm to select the proper parameters that can generate networks with given structural properties, the effectiveness of which is verified by fitting various real-world networks. Lastly, we explore the co-evolution of PAD model and higher-order contagion dynamics, and analytically derive the critical conditions for phase transition and bistable phenomenon. Our model provides a basic tool to reproduce complex structural properties and to study the widespread higher-order dynamics, which has great potential for applications across fields.

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