威尼斯人

摘要：Clustering on multilayer networks has been shown to be a promising approach to enhance accuracy. Various multilayer networks clustering algorithms assume all networks derive from a latent clustering structure and jointly learn the

compatible and complementary information from different networks to excavate one shared underlying structure. However, such assumptions are in conflict with many emerging real-life applications due to the existence of noisy/irrelevant

networks. A key challenge here is to integrate different data representations automatically to achieve better predictive performance. To address this issue, we propose Centroid-based Multilayer Network Clustering (CMNC), a novel approach which can divide irrelevant relationships into different network groups and uncover the cluster structure in each group simultaneously. The multilayer networks are represented within a unified tensor framework for simultaneously capturing multiple types of relationships between a set of entities. By imposing the rank-(Lr; Lr; 1) block term decomposition with nonnegativity constraints, we are able to have well interpretations on the multiple clustering results based on graph cut theory. Numerically, we transform this tensor decomposition problem to an unconstrained optimization, thus can solve it efficiently under the nonlinear least squares (NLS) framework. Extensive experimental results on synthetic and real-world datasets show the effectiveness and robustness of our method against noise and irrelevant data.  

报告人简介

任职于中山大学数据科学与计算机学院。2012 年于中山大学数学与应用数学专业获学士学位，2016 年于香港浸会大学数学统计专业获博士学位，2016-2017 年于比利时鲁汶大学电子工程系任博士后研究员，曾获香港 Yakun奖学金及比利时FWO博后奖学金。现任中国计算机学会(CCF)人工智能与模式识别专委会通讯委员。