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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
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publications
Disintegrate hypergraph networks by attacking hyperedge
Published in Journal of King Saud University - Computer and Information Sciences, 2022
According to study results, higher-order interactions are becoming more generally accepted as an essential element of complex systems. Hypergraphs may be used to explore the relationships between higher-order structures and functions in complex systems and capture higher-order interactions. After the initial failure of a hypergraph network, cascading failures may occur, just as they can with a simple network. Previous research has concentrated on random initial failures, and how hypergraph networks adapt to targeted attacks remains unanswered. In this research, we build a mathematical framework to explore the robustness of hypergraph networks against targeted attacks based on the magnitude of the hyperedge’s cardinality. We discovered that when the probability of large cardinality hyperedges being deleted grows, the network becomes more fragile.
Recommended citation: Hao Peng, Cheng Qian, Dandan Zhao, Ming Zhong, Xianwen Ling, Wei Wang* http://lingxianwen.github.io/files/paper1.pdf
Robustness of directed higher-order networks
Published in Chaos: An Interdisciplinary Journal of Nonlinear Science, 2023
We propose a theoretical percolation model to analyze the robustness of directed higher-order networks. We study the size of the giant connected components and the percolation threshold of our proposed model by the theory and Monte-Carlo simulations on artificial networks and real-world networks. We find that the percolation threshold is affected by the inherent properties of higher-order networks, including the heterogeneity of the hyperdegree distribution and the hyperedge cardinality, which represents the number of nodes in the hyperedge. Increasing the hyperdegree distribution of heterogeneity or the hyperedge cardinality distribution of heterogeneity in higher-order networks will make the network more vulnerable, weakening the higher-order network’s robustness. In other words, adding higher-order directed edges enhances the robustness of the systems. Our proposed theory can reasonably predict the simulations for percolation on artificial and real-world directed higher-order networks.
Recommended citation: Dandan Zhao; Xianwen Ling; Xiongtao Zhang; Hao Peng ; Ming Zhong ; Cheng Qian ; Wei Wang* http://lingxianwen.github.io/files/paper2.pdf
Robustness of interdependent directed higher-order networks against cascading failures
Published in Physica D: Nonlinear Phenomena, 2024
To understand the robustness of interdependent directed higherorder networks, we propose a new theoretical framework to model and analyze the robustness of such networks under random failures by percolation theory. We find that adding higher-order edges makes the network more vulnerable which quantifies and compares by two criteria: the size of the giant connected components and the percolation threshold. Increasing the hyperdegree distribution of heterogeneity or the hyperedge cardinality distribution of heterogeneity in interdependent directed higher-order networks will also make the network more vulnerable. Interestingly, the phase transition type changes from continuous to discontinuous with the increase of coupling strength, and partially interdependent directed higher-order networks exist hybrid phase transition. Moreover, by applying our theoretical analysis to real interdependent directed higher-order networks further validated our conclusion, it has implications for the design of flexible complex systems.
Recommended citation: Dandan Zhao, Xianwen Ling, Hao Peng, Ming Zhong Jianmin Han, Wei Wang∗ http://lingxianwen.github.io/files/paper3.pdf
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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