Network Science | Yiming Huang

网络科学 ｜ 黄一鸣

Welcome to my homepage.

Yiming Huang

Yiming Huang

PhD Candidate

Imperial College London

CIRCLE Group

About Me

I am Yiming Huang (黄一鸣), a PhD student and Lee Family scholar in the CIRCLE Group at Imperial College London, advised by Prof. Tolga Birdal. Previously, I obtained an MSc in Computer Science and Technology from the University of Electronic Science and Technology of China (UESTC), advised by Prof. Linyuan Lü.

My research interests primarily focus on developing novel topological deep learning methods for understanding complex and higher-order graph structures. I also interested in generative models, graph representation learning, higher-order network analysis, and their interdisciplinary applications.

If you have any academic problems or seeking any form of cooperation, please feel free to email me 😃

Interests

  • Network Science
  • Topological Deep Learning
  • Higher-order Network Analysis
  • Influence Maximization
  • Graph Representation Learning
  • Quantum Computation

Education

  • Studying in Computing Research

    Imperial College London

  • MSc in Computer Science and Technology, 2024

    University of Electronic Science and Technology of China (UESTC)

  • BSc in Computer Science and Technology, 2021

    Nanjing University of Information Science and Technology (NUIST)

Recent Publications

Influential Simplices Mining via Simplicial Convolutional Networks

Information Processing & Management. 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 Dataset DOI Preprint 公众号解读
Influential Simplices Mining via Simplicial Convolutional Networks

Identifying vital nodes through random walks on higher-order networks

Information Sciences. 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 Code DOI Preprint
Identifying vital nodes through random walks on higher-order networks

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.
PDF Code DOI Preprint YouTube Bilibili 公众号解读
Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes
See all publications

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)
post
Identifying key players in complex networks through network entanglement

Contact