*物理学报*. 本文综述了两种最常见的高阶网络——超图和单纯形网络——常用的统计指标及其物理意义. 本文有助于加深对高阶网络的理解, 促进对高阶网络结构特征的定量化研究, 也有助于研究者在此基础上开发更多适用于高阶网络的统计指标.
*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.
*Entropy.* This study presents a novel hyperedge swapping method to construct hyper-null models for hypergraphs, which preserves certain network properties while altering others, and demonstrates their applicability in assessing network randomness, statistical properties, and dynamics across multiple datasets.
Developed a cooperative network learning (CNL) framework using technologies like homomorphic encryption, enabling decentralized, multi-party trusted, and privacy-preserving graph learning.
*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.