Welcome to My Homepage. I’m Runhao Jiang (蒋润浩), a Ph.D student at Department of Computer Science in Hong Kong Baptist University (HKBU), advised by Prof. Renchi Yang starting from Sep. 2024. Before that, I received my B.S. degree in Mathematics and Applied Mathematics from Zhejiang Normal University (ZJNU) in 2024.
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Hong Kong Baptist UniversityDepartment of Computer Science
Ph.D. StudentSep. 2024 - present -
Zhejiang Normal UniversityB.S. in Mathematics and Applied MathematicsSep. 2020 - Jul. 2024
Honors & Awards
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Silver Medal, ICPC EC-Final2023
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National Scholarship for Undergraduates2022
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Gold Medal, ICPC2022
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Gold Medal, CCPC2022
News
Selected Publications (view all )
Effective Clustering for Large Multi-Relational Graphs
Xiaoyang Lin, Runhao Jiang, Renchi Yang
International Conference on Management of Data (SIGMOD) 2026 Spotlight
DEMM and DEMM+, two effective MRGC approaches to address the aforementioned limitations of existing solutions, and extend DEMM to handle attribute-less MRGs through non-trivial adaptations.
Effective Clustering for Large Multi-Relational Graphs
Xiaoyang Lin, Runhao Jiang, Renchi Yang
International Conference on Management of Data (SIGMOD) 2026 Spotlight
DEMM and DEMM+, two effective MRGC approaches to address the aforementioned limitations of existing solutions, and extend DEMM to handle attribute-less MRGs through non-trivial adaptations.
Community-Aware Social Community Recommendation
Runhao Jiang, Renchi Yang, Wenqing Lin
International Conference on Information and Knowledge Management (CIKM) 2025 Spotlight
CASO is presented, a novel and effective model specially designed for social community recommendation that includes a community detection loss in the model optimization, thereby producing community-aware embeddings for communities.
Community-Aware Social Community Recommendation
Runhao Jiang, Renchi Yang, Wenqing Lin
International Conference on Information and Knowledge Management (CIKM) 2025 Spotlight
CASO is presented, a novel and effective model specially designed for social community recommendation that includes a community detection loss in the model optimization, thereby producing community-aware embeddings for communities.