With the rapid development of e-commerce and social media, massive user-centred data is generated from various platforms. Most of the generated data can be represented in the forms of graph, which is capable to demonstrate the complicated relations among various entities, for example, graphs describe the interactions history between users and items. It is critical for the platforms to mine graph data to formulate recommendation strategy to gain more profits.

For instance, in a user-item interaction graph, we can utilize graph data mining techniques to capture users’ behavioral patterns to make personalized recommendation strategies. Graph data mining is currently a hot research topic that attracts more and more attentions from industry and academic fields. In this talk, I will present some of our recent graph mining methods and its applications in real world systems.

This session will be conducted online via Zoom:


Dr Rocky Chen


Dr. Hongxu Chen is a data scientist with Commonwealth Bank of Australia (CBA). Prior to that, he was a Postdoc Research Fellow at UTS.

Hongxu completed his Ph.D. at The University of Queensland (UQ), Australia in Data Science (formerly DKE) Research Group, School of Information Tech. and Electrical Engineering (ITEE). His PhD Thesis won the 2020 Dean's Award for Outstanding Higher Degree by Research Theses.

Hongxu’s research interests mainly focus on data science in general and expend across multiple practical application scenarios, such as network science, data mining, recommendation systems and social network analytics. In particular, his research is focusing on learning representations for information networks and applying the learned network representations to solve real-world problems in complex networks such as  biology (rare disease prediction via patient-symptoms graph), e-commerce (e.g., customer behaviour modelling and prediction, sales prediction) and social networks, financial market (e.g., information diffusion & trading behaviour) and recommendations systems with heterogenous information sources. Hongxu has published more than 40 peer-reviewed papers in top-tier high-quality international conferences and journals, such as SIGKDD, ICDE, ICDM, AAAI, IJCAI, TKDE.

Hongxu also serves as program committee member and reviewers in multiple international conference, such as CIKM, ICDM, KDD, SIGIR, AAAI, PAKDD, WISE, and he also acts as invited reviewer for multiple journals in his research fields, including Transactions on Knowledge and Data Engineering (TKDE), WWW Journal, VLDB Journal, IEEE Transactions on Systems, Man and Cybernetics: Systems, Journal of Complexity, ACM Transactions on Data Science, Journal of Computer Science and Technology.


About Data Science Seminar

This seminar series will be run as weekly sessions and is hosted by ITEE Data Science.



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