Researcher and Educator

My scholar interests are centered on graph theory and its pivotal applications in complex network analysis, pattern recognition, and knowledge graph completion. My research is specifically concentrated on addressing the principal challenges within this domain: link prediction, influential node identification, and community detection. These areas have been rigorously explored, yielding a plethora of algorithms for predicting information within networks. My objective is to innovate and develop groundbreaking algorithms by harnessing the capabilities of graph convolutional networks (GCN), graph neural networks (GNN), graph attention networks (GAT), deep neural networks (DNN), and sophisticated machine learning techniques. The aim is to surpass the current benchmarks set by state-of-the-art methodologies in link prediction and community detection, as well as tackle associated challenges across various interdisciplinary fields.