On April 14, 2025, the Cross_Media Big Data Joint Laboratory invited Professor Wu Danyang from the School of Information Engineering and Associate Professor Dang Qianlong from the School of Science at Northwest A&F University to deliver academic lectures titled ''A Comprehensive Perspective on Multi-Graph Clustering'' and ''Graph Neural Network-Assisted Evolutionary Algorithms for Multimodal Multi-Objective Optimization'', respectively. The scholars engaged in in-depth exchanges with laboratory faculty and students attending the lectures.
Professor Wu Danyang shared his recent research progress and methodologies in multi-graph clustering, demonstrating its potential to address challenges of complex data structures across interdisciplinary domains such as life sciences, computer vision, and social sciences.
Professor Dang Qianlong focused on solving multimodal multi-objective optimization problems (MMOPs), introducing graph neural network (GNN)-assisted evolutionary algorithms. His presentation covered limitations of traditional evolutionary algorithms for MMOPs, the design of GNN-based evolutionary frameworks, and how graph learning enhances evolutionary processes by capturing topological relationships in solution spaces.
After the report, the laboratory teachers and students actively asked questions and interacted with the two professors for communication.
[Editor: Yaoguang Zhang]