Network Science | Yiming Huang

网络科学 ｜ 黄一鸣

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Yiming Huang

Yiming Huang

Undergraduate

University of Electronic Science and Technology of China

CSSC team

About Me

I am Yiming Huang (黄一鸣), a master student at the Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China (UESTC), advised by Prof. Linyuan Lü.

My primary research interests include higher-order network analysis, topological deep learning, influential maximization and their intersections. If you have any academic problems or seeking any form of cooperation, please feel free to email me at yiming_huang@std.uestc.edu.cn 😃

I am looking for a Ph.D. position in relative area.

Interests

  • Network Science
  • Higher-order Network
  • Vital Node Identification
  • Graph Representation Learning
  • Topological Deep Learning
  • Quantum Computation

Education

  • Studing in Computer Science and Technology, 2021

    University of Electronic Science and Technology of China (UESTC)

  • BSc in Computer Science and Technology, 2017

    Nanjing University of Information Science and Technology (NUIST)

Recent Publications

Identifying key players in complex networks via network entanglement

Communications Physics. Proposed an entanglement-based metric - vertex entanglement (VE) - quantifying local perturbations on spectral entropy, with superior applications in network dismantling and brain network analysis.
Yiming Huang, Hao Wang, Xiao-Long Ren, Linyuan Lü
PDF Code DOI 公众号解读
Identifying key players in complex networks via network entanglement

Cooperative Network Learning for Large-Scale and Decentralized Graphs

Developed a cooperative network learning (CNL) framework using technologies like homomorphic encryption, enabling decentralized, multi-party trusted, and privacy-preserving graph learning.
Qiang Wu, Yiming Huang, Yujie Zeng, Yijie Teng, Fang Zhou, Linyuan Lü, (Co-first author)
PDF Code
Cooperative Network Learning for Large-Scale and Decentralized Graphs

Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes

AAAI24. Introduced a novel higher-order representation, the flower-petals (FP) model, and higher-order graph convolutional network (HiGCN), which achieves SOTA in various tasks and quantifies higher-order strength.
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Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes

Influential Simplices Mining via Simplicial Convolutional Network

Detected an inconsistency between mining influential nodes and simplices. Innovatively formulated the influential simplices mining task as a graph learning problem and designed an influential simplices mining neural network (ISMnet) that achieves SOTA performance in this task.
Yujie Zeng, Yiming Huang, Qiang Wu, Linyuan Lü, (Co-first author)
PDF
Influential Simplices Mining via Simplicial Convolutional Network

Identifying vital nodes through random walks on higher-order networks

Developed a Higher-order Augmented Random Walk (HoRW) model to identify influencers, enabling multi-scale analysis according to the strength of higher-order effects.
Yujie Zeng, Yiming Huang, Xiao-Long Ren, Linyuan Lü, (Co-first author)
PDF
Identifying vital nodes through random walks on higher-order networks
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Talks

Influential Simplices Mining via Simplicial Convolutional Network

Influential Simplices Mining with higher-order GNN model
Jul 12, 2023 Vienna, Austria
PDF

HiGCN: Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes

Higher-order Graph Convolutional Network
May 11, 2023 Chongqing, China
PDF

Identifying key players in complex networks through network entanglement

Vital node identification
Jul 28, 2022 Shanghai (Online)
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Identifying key players in complex networks through network entanglement

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