Yizhou Sun

About

Yizhou Sun is a professor in the Department of Computer Science at UCLA Samueli School of Engineering. His research interests include data mining, database systems, information retrieval, machine learning, and network science. He earned his PhD from the University of Illinois at Urbana-Champaign in 2012. Throughout his career, he has received numerous awards, including the Data Mining Test of Time Award in 2024, the SDM / IBM Early Career Data Mining Research Award in 2023, the IEEE Intelligent Systems Top 10 Rising Stars in 2023, the VLDB Test of Time Award in 2022, and the NSF Career Award in 2015. His work has been recognized for its significant contributions to the field of data science and related areas.

Research topics

• Computer Science
• Artificial Intelligence
• Data Mining
• Machine Learning
• Theoretical computer science
• Data science
• Distributed computing
• Information Retrieval
• Computer Security
• Mathematics

Selected publications

• Heterogeneous Graph Transformer

  2020 · 1284 citations

  Senior authorCorresponding

  Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data. However, most GNNs are designed for homogeneous graphs, in which all nodes and edges belong to the same types, making it infeasible to represent heterogeneous structures. In this paper, we present the Heterogeneous Graph Transformer (HGT) architecture for modeling Web-scale heterogeneous graphs. To model heterogeneity, we design node- and edge-type dependent parameters to characterize t…

  DOI

• GPT-GNN

  2020 · 437 citations

  Senior authorCorresponding

  Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data. However, training GNNs requires abundant task-specific labeled data, which is often arduously expensive to obtain. One effective way to reduce the labeling effort is to pre-train an expressive GNN model on unlabelled data with self-supervision and then transfer the learned model to downstream tasks with only a few labels. In this paper, we present the GPT-GNN framework to initialize GNNs by gene…

  DOI

• Finding key players in complex networks through deep reinforcement learning

  Nature Machine Intelligence · 2020 · 397 citations

  DOI

• Heterogeneous Network Representation Learning: A Unified Framework With Survey and Benchmark

  IEEE Transactions on Knowledge and Data Engineering · 2020 · 311 citations

  . from different sources, towards handy and fair evaluations of HNE algorithms. As the third contribution, we carefully refactor and amend the implementations and create friendly interfaces for 13 popular HNE algorithms, and provide all-around comparisons among them over multiple tasks and experimental settings. By putting all existing HNE algorithms under a unified framework, we aim to provide a universal reference and guideline for the understanding and development of HNE algorithms. Meanwhile…

  DOI

• P-Companion

  2020 · 66 citations

  If one customer buys a tennis racket, what are the best 3 complementary products to purchase together? 3 tennis ball packs, 3 headbands, 3 overgrips, or 1 of each respectively? Complementary product recommendation (CPR), aiming at providing product suggestions that are often bought together to serve a joint demand, forms a pivotal component of e-commerce service, however, existing methods are far from optimal. Given one product, how to recommend its complementary products of different types is t…

  DOI

Recent grants

• III: Medium: Collaborative Research: StructNet: Constructing and Mining Structure-Rich Information Networks for Scientific Research

  NSF · $412k · 2017–2022

• CAREER: Mining and Exploring Heterogeneous Information Networks with Social Factors

  NSF · $377k · 2016–2021

• CAREER: Mining and Exploring Heterogeneous Information Networks with Social Factors

  NSF · $192k · 2015–2017

Frequent coauthors

• Jiawei Han

  University of Illinois Urbana-Champaign

  71 shared

• Wei Wang

  University of Electronic Science and Technology of China

  49 shared

• Yunsheng Bai

  University of California, Los Angeles

  44 shared

• Kewei Cheng

  27 shared

• Ziniu Hu

  25 shared

• Kai-Wei Chang

  24 shared

• Zijie Huang

  East China University of Science and Technology

  20 shared

• Ting Chen

  University of Electronic Science and Technology of China

  19 shared

Awards & honors

• Data Mining Test of Time Award, 2024
• SDM / IBM Early Career Data Mining Research Award, 2023
• IEEE Intelligent Systems Top 10 Rising Stars, 2023
• VLDB Test of Time Award, 2022
• Amazon Research Award, 2020

Similar researchers at University of California, Los Angeles

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