Prof. Canzhong Yao

South China University of Technology, China

Chair of the Department of Economics

Brief: Professor and Doctoral Supervisor at the School of Economics and Finance. His main research fields include digital economy, platform economy, systems engineering, complex systems and complex networks. He has presided over more than 10 research projects funded by the National Social Science Fund of China, National Natural Science Foundation of China, and Humanities and Social Science Fund of the Ministry of Education, and has independently published one academic monograph.

Prof. Shenghui Cheng 

Ji'nan University, China

Brief: Cheng Shenghui holds a Doctor of Philosophy in Computer Science from Stony Brook University, State University of New York. He is currently a Professor and Doctoral Supervisor at the School of Journalism and Communication and the Big Data Center of Jinan University, as well as the Deputy Director of the AGI Intelligent Media Application R&D Center. He has published more than 50 high-level papers categorized as CCF Class A, CAS Zone 1, SCI Q1 and CSSCI, which have been cited over 1,300 times according to Google Scholar. He has authored over 20 monographs in Chinese and English (mostly as the sole author) with a total word count of nearly 10 million, and possesses more than 10 invention patents in China and the United States.

Title: Hamiltonian cycle clustering with asymmetric correlation

Abstract: Analysts who explore high-dimensional data usually want three answers at once: Which samples belong together, how close the resulting groups are, and who influences whom accordingly. Classical clustering provides only hard labels, hiding both inter-cluster affinities and correlation flow. We introduce Hamiltonian Cycle Clustering with Asymmetric Correlation HCC-AC, a framework that converts the clustering task into an interpretable map where structure and directionality are visible at a single glance. HCC-AC first learns soft memberships by optimizing a joint global-local loss, preserving manifold structure while turning each label into a probability. These probabilities drive a Hamiltonian-cycle embedding: cluster anchors are ordered by affinity and placed evenly on a circle; samples fall radially towards their most-likely anchor, so clusters, their similarities (arc lengths), and outliers emerge immediately. Directed arrows connect anchors, their lengths showing correlation strength, transforming the map into a legible narrative of influence. Experiments on five benchmark datasets demonstrate that HCC-AC improves the knowledge discovery in clustering, i.e., indexes the clustering results, flags outliers reliably, and uncovers correlation pathways. (c) 2025 The Author(s). Published by Elsevier B.V. on behalf of Zhejiang University and Zhejiang University Press. This is an open access article under the CC BY license.