Source Themes

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.

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.

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.

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.

A novel coherence-based quantum steganalysis protocol

We proposed a novel coherence-based quantum steganalysis protocol to detect quantum steganography by comparing detected and theoretical distributions.