Events

Qualifying Exam Predictive Cyber-Physical Systems via Heterogeneous Graphs
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Speaker: Jinquan Hang

Bio

Location : CoRE 305

Committee: 

Associate Professor Desheng Zhang

Assistant Professor Dong Deng

Associate Professor Yongfeng Zhang

Assistant Professor Sumegha Garg

Event Type: Qualifying Exam

Abstract: Cyber-physical systems (CPS) are a new information paradigm connecting the physical and cyber worlds. In CPS, we collect data from the physical world, analyze it in the cyber world, and make real-time decisions to improve the physical world. This closes the loop between the physical and cyber worlds. My work focuses on CPS via Heterogeneous Graphs to improve real-world customer experience in an industry setting. According to a recent study by Gartner, customer experience is the new battleground for business, with 81% of companies expecting to compete mostly or entirely based on customer experience by 2025.My research vision follows the CPS life cycle for customer services. I collected spatial-temporal logistics, customer, and company data from the physical world. This data is then transformed into a billion-scale heterogeneous spatial-temporal graph, and we use heterogeneous graph neural networks (HGNNs) to mine information for customer service applications. These applications, such as company key personnel detection, improve customer service through HGNNs' prediction results. The improved customer service generates new data, which is used by HGNNs to further improve prediction accuracy. In this talk, I will focus on my paper, "Paths2Pair: Meta-path Based Link Prediction in Billion-Scale Commercial Heterogeneous Graphs," which was accepted by KDD 2024. Paths2Pair can effectively discover potential company key personnel relationships from billion-scale heterogeneous graphs based on known relationships and has helped a major company identify 108,709 potential key personnel of companies.Papers:Paths2Pair: Meta-path Based Link Prediction in Billion-Scale Commercial Heterogeneous Graphs, Accepted by KDD 2024Complex-Path: Effective and Efficient Node Ranking with Paths in Billion-Scale Heterogeneous Graphs, Accepted by VLDE 2024

Organization: 

Contact  Associate Professor Desheng Zhang

https://rutgers.zoom.us/j/5034279181?pwd=U2FFOGIwVkJJV1dHbzYxK2VTVDBiZz09