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Gang Yan

4 papers hereh-index 6100 citations16 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author2
  • last author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.AI1
  • cs.LG1
  • nlin.AO1
  • physics.soc-ph1
same name
  • Gang Yan — 7 papers, h 10
  • Gang Yan — 4 papers, h 7
  • Gang Yan — 4 papers, h 2
  • Gang Yan — 3 papers, h 3
  • Gang Yan — 3 papers, h 2
  • Gang Yan — 2 papers, h 7

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20222026
most citedNon-parametric power-law surrogates

7 citations · 10 across the 4 of their papers we have counts for

collaborators

4 papers

physics.soc-ph2026

Low-Dimensional Phase Diagram of Higher-Order Networked Systems

Jia-Jie Qin, Jack Murdoch Moore, Xiaozhu Zhang +1

Higher-order networks exhibit rich critical phenomena that cannot be captured by traditional pairwise models. Here, we develop an analytical dimension-reduction framework that maps…

cs.LG2024

Maintaining Adversarial Robustness in Continuous Learning

Xiaolei Ru, Xiaowei Cao, Zijia Liu +5

Adversarial robustness is essential for security and reliability of machine learning systems. However, adversarial robustness enhanced by defense algorithms is easily erased as the…

cs.AI2022★ 3 cited

Identifying Unique Spatial-Temporal Bayesian Network without Markov Equivalence

Mingyu Kang, Duxin Chen, Ning Meng +2

Identifying vanilla Bayesian network to model spatial-temporal causality can be a critical yet challenging task. Different Markovian-equivalent directed acyclic graphs would be ide…

nlin.AO2022★ 7 cited

Non-parametric power-law surrogates

Jack Murdoch Moore, Gang Yan, Eduardo G. Altmann

Power-law distributions are essential in computational and statistical investigations of extreme events and complex systems. The usual technique to generate power-law distributed d…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.