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Abstract

Ph.D. candidate, supervised by Prof. Zhewei Wei
Gaoling School of Artificial Intelligence, Renmin University of China
Tel: +86-183f6ff9ff8 (f=5)
Email: dinghaipeng AT ruc.edu.cn
One page CV: [Chinese][English]

1. Introduction

I am Haipeng Ding(丁海鹏), a 3rd-year Ph.D. candidate at Gaoling School of Artificial Intelligence, Renmin University of China. I am fortunated to be supervised by Prof. Zhewei Wei, and be a member of the ALGO Lab. Before my graduate studies, I received my B.E. degree in Computer Science and Technology at Turing Class, School of Information, Renmin University of China in June 2022. Meanwhile, I participated the honorary minor of Intelligent Big Data major, and successfully graduated.

My research interests primarily focus on large-scale and scalable graph machine learning, graph computing, and currently the combination of graph learning and large language models, and graph LLM-based agents.

2. Preliminaries

MY RECENT NEWS will be listed in this section.

Jan 17, 2025: 🎇🎆 As one of the main contributors, I am honored to declear that we have released “Jittor Geometric” graph learning library at this repository. As a Chinese-developed library, it aims to provide an efficient and flexible GNN implementation for researchers and engineers working with graph-structured data.
Dec 18, 2024: Thanks to AITIME, I have the opportunity to give a talk and share my work “Large-Scale Spectral Graph Neural Networks via Laplacian Sparsification”. The video is available here.
Dec 09, 2024: My first authored paper “Scalable and Effective Graph Neural Networks via Trainable Random Walk Sampling” has been officially published.
Nov 25, 2024: 🥂🥂 My first authored paper “Scalable and EffectiveGraph Neural Networks via Trainable Random Walk Sampling” has been accepted by TKDE 2025. This takes a long time and finally rewards💦. The codes here are available.
Nov 17, 2024: 🥂🥂 My first authored paper “Large-Scale Spectral Graph Neural Networks via Laplacian Sparsification” has been accepted by KDD 2025. As the official publication could be time consuming, we release our technical report and codes.

MY PUBLICATIONS will be listed in this section.

First-authored papers.

KDD 2025	Large-Scale Spectral Graph Neural Networks via Laplacian Sparsification Haipeng Ding, Zhewei Wei, and Yuhang Ye In Proceedings of 31th ACM SIGKDD Conference on Kowledge Discovery and Data Mining, 2025* [Paper] [Code] [arXiv] [Talk]
TKDE 2025	Scalable and Effective Graph Neural Networks via Trainable Random Walk Sampling Haipeng Ding, Zhewei Wei, and Yuhang Ye IEEE Transaction on Knowledge and Data Engineering, 2025* [Paper] [Code]

Co-authored works.

Preprint	Exploring the Potential of Large Language Models as Predictors in Dynamic Text-Attributed Graphs Runlin Lei, Jiarui Ji, Haipeng Ding, Lu Yi, Zhewei Wei, Yongchao Liu, Chuntao Hong ArXiv preprint [ArXiv]
TPDS 2023	Enabling Efficient Random Access to Hierarchically Compressed Text Data on Diverse GPU Platforms Yihua Hu, Feng Zhang, Yifei Xia, Zhiming Yao, Letian Zeng, Haipeng Ding, Zhewei Wei, Xiao Zhang, Jidong Zhai, Xiaoyong Du IEEE Transaction on Parallel and Distributed Systems* [Paper]
SC 2022	Optimizing random access to hierarchically-compressed data on GPU Feng Zhang, Yihua Hu, Haipeng Ding, Zhiming Yao, Zhewei Wei, Xiao Zhang, Xiaoyong Du In Proceedings of the International Conference for High Performance Computing, Networking, Strorage and Analysis, SC22* [Paper]
AI Open 2021	Neural, symbolic and neural-symbolic reasoning on knowledge graphs Jing Zhang, Bo Chen, Lingxi Zhang, Xirui Ke, and Haipeng Ding* AI Open, 2021 [Paper]

4. Selected Awards

(I don’t know how to name this section as an academic paper😂.)

2019	Gold Medal in ICPC Asia Regional Contest. I won two gold medals in Nanjing and Nanchang.
2021	Silver Medal in ICPC Asia-East Continent Final. I won two silver medals in 2019 and 2021.
2022	Outstanding Graduate Student of Renmin University of China
2021	CCF Elite Collegiate Award
2019	First Class Academic Excellence Scholarship

As the space limits, you can retrieve the whole list of my honors here.

5. Experiments

5.1 Research Experience

From Sept. 2018 to Jun. 2022, in Computer Science and Technology at Turing Class, School of Information, Renmin University of China. I successfully finished my undergraduate studies, and received B.E. degree.
- From Oct. 2019 to Mar. 2021, in DWBI Lab. Leader: Jing Zhang. My research life began here. I learned how to read papers, and how to do research. My research direction is knowledge graph question answering.
- From Mar. 2021 to May. 2021, in CoAI Lab. Leader: Minlie Huang.
  I am honored to have the chance for a short-term exchange study. I try to link open-domain textual information and structured knowledge graph, and complete the retrieval and reasoning.
- From May. 2021 to June. 2022, in ALGO Lab. Leader: Zhewei Wei.
From Sept. 2022 to now, in Gaoling School of Artificial Intelligence, Renmin University of China.
I am currently a 3rd-year Ph.D. candidate. I am fortunated to be supervised by Prof. Zhewei Wei, and be a member of the ALGO Lab, since May. 2021. My supervisor’s kindness of mentorship and enlightment has illuminated my path, for which I shall remain eternally grateful.
- I have participated in Huawei-Renmin University joint program on Information Retrieval for over 3 years.

Some of my services will be listed in this section, which may reflect some of my distinctive skills.

I serve(d) as a(n) (invited) reviewer for:

ICDE2025, ICML2025;
NIPS2024, PAKDD2024, WSDM2024;
NIPS2023, ICDE2022, KDD2022, TKDE2021, and some earlier reviews.

I play(ed) some important roles in/as:

Main contributor of the graph learning library Jittor Geometric;
Textbook compilation (part of algorithm design and analysis) of Project 101;
Server administrating, problem fixing, and daily maintainence, of our research group;
Computer clinics, hosted by the Computer Associates of Renmin University of China;
Psychological member of my undergraduate class.

6. Conclusion

This homepage is a brief elaboration about my academic achievements and working capabilities. If you are interested in, and get to know me more deeply, you may visit this page, and feel free to contact me in any way.

I am currently seeking visitor / exchange / internship opportunities to collaborate with leading researchers and institutions in the field of graph machine learning, large language models, and multi-agent. I am eager to engage in joint projects, exchange ideas, and contribute to innovative research initiatives.