Hanlin is a final year Ph.D. student at Queen Mary University of London, working on network theory. His research focuses on several aspects of dynamic processes on networks and other structures with higher-order interactions, such as simplicial complexes and hypergraphs, under the supervision of Prof. Ginestra Bianconi. His current Ph.D. project aims to understand the critical properties of standard and higher-order percolation on networks and hypergraphs respectively, and how network topologies and geometries affect the robustness and other critical behaviors.
Before joining Queen Mary, Hanlin studied physics at the University of Chinese Academy of Sciences, China (BSc). During his undergraduate study, he worked on the inference and optimization on multiple interacting spreading processes on networks under the supervisor of Prof. David Saad, Aston University, and low-rank approximation algorithms on tensor networks under the supervisor of Prof. Pan Zhang, Institute of Theoretical Physics, Chinese Academy of Sciences.
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PhD in Applied Mathematics, 2023
Queen Mary University of London, UK
BSc in Physics, 2019
University of Chinese Academy of Sciences, China
Visiting student, 2018
KTH Royal Institute of Technology, Sweden
Visiting student, 2018
Aston University, UK
I have been a Teaching Associate at Queen Mary University of London for the following course:
I have been a Graduate Teaching Assistant at King’s College London for the following course:
I have been an organiser of the following events:
I have given contributed and invited talks on the following conferences:
I have also given talks on other internal seminars:
I have been a reviewer for the following journals: Physica A: Statistical Mechanics and its Applications, Communication Physics, Scientific Reports, New Journal of Physics, IEEE Transactions on Network Science and Engineering, Bioinformatics, Chaos Solitons and Fractals, Journal of Physics A: Mathematical and Theoretical, Chaos: An Interdisciplinary journal of Nonlinear Science
Now I serve as a Guest Editor Assistant of the Special Issue “Models, Topology and Inference of Multilayer and Higher-Order Networks” in Entropy.
Percolation establishes the connectivity of complex networks and is one of the most fundamental critical phenomena for the study of complex systems. On simple networks, percolation displays a second-order phase transition; on multiplex networks, the percolation transition can become discontinuous. However, little is known about percolation in networks with higher-order interactions. Here, we show that percolation can be turned into a fully fledged dynamical process when higher-order interactions are taken into account. By introducing signed triadic interactions, in which a node can regulate the interactions between two other nodes, we define triadic percolation. We uncover that in this paradigmatic model the connectivity of the network changes in time and that the order parameter undergoes a period doubling and a route to chaos. We provide a general theory for triadic percolation which accurately predicts the full phase diagram on random graphs as confirmed by extensive numerical simulations. We find that triadic percolation on real network topologies reveals a similar phenomenology. These results radically change our understanding of percolation and may be used to study complex systems in which the functional connectivity is changing in time dynamically and in a non-trivial way, such as in neural and climate networks.
The collocation of individuals in different environments is an important prerequisite for exposure to infectious diseases on a social network. Standard epidemic models fail to capture the potential complexity of this scenario by (1) neglecting the higher-order structure of contacts that typically occur through environments like workplaces, restaurants, and households, and (2) assuming a linear relationship between the exposure to infected contacts and the risk of infection. Here, we leverage a hypergraph model to embrace the heterogeneity of environments and the heterogeneity of individual participation in these environments. We find that combining heterogeneous exposure with the concept of minimal infective dose induces a universal nonlinear relationship between infected contacts and infection risk. Under nonlinear infection kernels, conventional epidemic wisdom breaks down with the emergence of discontinuous transitions, superexponential spread, and hysteresis.
With the hit of new pandemic threats, scientific frameworks are needed to understand the unfolding of the epidemic. The use of mobile apps that are able to trace contacts is of utmost importance in order to control new infected cases and contain further propagation. Here we present a theoretical approach using both percolation and message-passing techniques, to the role of contact tracing, in mitigating an epidemic wave. We show how the increase of the app adoption level raises the value of the epidemic threshold, which is eventually maximized when high-degree nodes are preferentially targeted. Analytical results are compared with extensive Monte Carlo simulations showing good agreement for both homogeneous and heterogeneous networks. These results are important to quantify the level of adoption needed for contact-tracing apps to be effective in mitigating an epidemic.
Competition and collaboration are at the heart of multiagent probabilistic spreading processes. The battle for public opinion and competitive marketing campaigns are typical examples of the former, while the joint spread of multiple diseases such as HIV and tuberculosis demonstrates the latter. These spreads are influenced by the underlying network topology, the infection rates between network constituents, recovery rates, and, equally important, the interactions between the spreading processes themselves. Here, for the first time, we derive dynamic message-passing equations that provide an exact description of the dynamics of two, interacting, unidirectional spreading processes on tree graphs, and we develop systematic low-complexity models that predict the spread on general graphs. We also develop a theoretical framework for the optimal control of interacting spreading processes through optimized resource allocation under budget constraints and within a finite time window. Derived algorithms can be used to maximize the desired spread in the presence of a rival competitive process and to limit the spread through vaccination in the case of coupled infectious diseases. We demonstrate the efficacy of the framework and optimization method on both synthetic and real-world networks.