A Parallel Page Cache: IOPS and Caching for Multicore Systems. - Da Zheng 004; Randal C. Burns; Alexander S. Szalay (HotStorage, 2012)

Toward millions of file system IOPS on low-cost, commodity hardware. - Da Zheng 004; Randal C. Burns; Alexander S. Szalay (SC, 2013)

Active Community Detection in Massive Graphs. - Heng Wang; Da Zheng 004; Randal C. Burns; Carey E. Priebe (CoRR, 2014)

FlashGraph: Processing Billion-Node Graphs on an Array of Commodity SSDs. - Da Zheng 004; Disa Mhembere; Randal C. Burns; Alexander S. Szalay (CoRR, 2014)

Hadoop in Low-Power Processors. - Da Zheng 004; Alexander S. Szalay; Andreas Terzis (CoRR, 2014)

FlashGraph: Processing Billion-Node Graphs on an Array of Commodity SSDs. - Da Zheng 004; Disa Mhembere; Randal C. Burns; Joshua T. Vogelstein; Carey E. Priebe; Alexander S. Szalay (FAST, 2015)

Optimize Unsynchronized Garbage Collection in an SSD Array. - Da Zheng 004; Randal C. Burns; Alexander S. Szalay (CoRR, 2015)

NUMA-optimized In-memory and Semi-external-memory Parameterized Clustering. - Disa Mhembere; Da Zheng 004; Joshua T. Vogelstein; Carey E. Priebe; Randal C. Burns (CoRR, 2016)

An SSD-based eigensolver for spectral analysis on billion-node graphs. - Da Zheng 004; Randal C. Burns; Joshua T. Vogelstein; Carey E. Priebe; Alexander S. Szalay (CoRR, 2016)

Semi-External Memory Sparse Matrix Multiplication on Billion-node Graphs in a Multicore Architecture. - Da Zheng 004; Disa Mhembere; Vince Lyzinski; Joshua T. Vogelstein; Carey E. Priebe; Randal C. Burns (CoRR, 2016)

FlashMatrix: Parallel, Scalable Data Analysis with Generalized Matrix Operations using Commodity SSDs. - Da Zheng 004; Disa Mhembere; Joshua T. Vogelstein; Carey E. Priebe; Randal C. Burns (CoRR, 2016)

knor: A NUMA-Optimized In-Memory, Distributed and Semi-External-Memory k-means Library. - Disa Mhembere; Da Zheng 004; Carey E. Priebe; Joshua T. Vogelstein; Randal C. Burns (HPDC, 2017)

Semi-External Memory Sparse Matrix Multiplication for Billion-Node Graphs. - Da Zheng 004; Disa Mhembere; Vince Lyzinski; Joshua T. Vogelstein; Carey E. Priebe; Randal C. Burns (IEEE Trans. Parallel Distributed Syst., 2017)

FlashR: parallelize and scale R for machine learning using SSDs. - Da Zheng 004; Disa Mhembere; Joshua T. Vogelstein; Carey E. Priebe; Randal C. Burns (PPoPP, 2018)

Challenges Towards Deploying Data Intensive Scientific Applications on Extreme Heterogeneity Supercomputers. - Hang Liu 001; Yufei Ding 001; Da Zheng 004; Seung Woo Son 001; Da Yan 001 (CoRR, 2018)

clusterNOR: A NUMA-Optimized Clustering Framework. - Disa Mhembere; Da Zheng 004; Carey E. Priebe; Joshua T. Vogelstein; Randal C. Burns (CoRR, 2019)

Graphyti: A Semi-External Memory Graph Library for FlashGraph. - Disa Mhembere; Da Zheng 004; Carey E. Priebe; Joshua T. Vogelstein; Randal C. Burns (CoRR, 2019)

Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs. - Minjie Wang; Lingfan Yu; Da Zheng 004; Quan Gan; Yu Gai; Zihao Ye 001; Mufei Li; Jinjing Zhou; Qi Huang 001; Chao Ma 025; Ziyue Huang; Qipeng Guo; et al. (CoRR, 2019)

Scalable Graph Neural Networks with Deep Graph Library. - Da Zheng 004; Minjie Wang; Quan Gan; Zheng Zhang 001; George Karypis (KDD, 2020)

FeatGraph: a flexible and efficient backend for graph neural network systems. - Yuwei Hu; Zihao Ye 001; Minjie Wang; Jiali Yu; Da Zheng 004; Mu Li 003; Zheng Zhang 001; Zhiru Zhang; Yida Wang 003 (SC, 2020)

DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs. - Da Zheng 004; Chao Ma 025; Minjie Wang; Jinjing Zhou; Qidong Su; Xiang Song 003; Quan Gan; Zheng Zhang 001; George Karypis (IA3@SC, 2020)

DGL-KE: Training Knowledge Graph Embeddings at Scale. - Da Zheng 004; Xiang Song 003; Chao Ma 025; Zeyuan Tan; Zihao Ye 001; Jin Dong; Hao Xiong; Zheng Zhang 001; George Karypis (SIGIR, 2020)

Learning Graph Neural Networks with Deep Graph Library. - Da Zheng 004; Minjie Wang; Quan Gan; Zheng Zhang 001; George Karypis (WWW (Companion Volume), 2020)

Collective Multi-type Entity Alignment Between Knowledge Graphs. - Qi Zhu 008; Hao Wei; Bunyamin Sisman; Da Zheng 004; Christos Faloutsos; Xin Luna Dong; Jiawei Han 001 (WWW, 2020)

Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing. - Vassilis N. Ioannidis; Da Zheng 004; George Karypis (CoRR, 2020)

PanRep: Universal node embeddings for heterogeneous graphs. - Vassilis N. Ioannidis; Da Zheng 004; George Karypis (CoRR, 2020)

Schema-Aware Deep Graph Convolutional Networks for Heterogeneous Graphs. - Saurav Manchanda; Da Zheng 004; George Karypis (IEEE BigData, 2021)

Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs. - Jialin Dong; Da Zheng 004; Lin F. Yang; George Karypis (KDD, 2021)

Dr. Top-k: delegate-centric Top-k on GPUs. - Anil Gaihre; Da Zheng 004; Scott Weitze; Lingda Li; Shuaiwen Leon Song; Caiwen Ding; Xiaoye S. Li; Hang Liu 001 (SC, 2021)

Learning over Families of Sets - Hypergraph Representation Learning for Higher Order Tasks. - Balasubramaniam Srinivasan; Da Zheng 004; George Karypis (SDM, 2021)

Scalable Graph Neural Networks with Deep Graph Library. - Da Zheng 004; Minjie Wang; Quan Gan; Xiang Song 003; Zheng Zhang 001; George Karypis (WSDM, 2021)

TraverseNet: Unifying Space and Time in Message Passing. - Zonghan Wu; Da Zheng 004; Shirui Pan; Quan Gan; Guodong Long; George Karypis (CoRR, 2021)

Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs. - Da Zheng 004; Xiang Song 003; Chengru Yang; Dominique LaSalle; Qidong Su; Minjie Wang; Chao Ma 025; George Karypis (CoRR, 2021)

Nimble GNN Embedding with Tensor-Train Decomposition. - Chunxing Yin; Da Zheng 004; Israt Nisa; Christos Faloutsos; George Karypis; Richard W. Vuduc (KDD, 2022)

Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Heterogeneous Graphs. - Da Zheng 004; Xiang Song 003; Chengru Yang; Dominique LaSalle; George Karypis (KDD, 2022)

TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs. - Hongkuan Zhou; Da Zheng 004; Israt Nisa; Vasileios Ioannidis; Xiang Song 003; George Karypis (CoRR, 2022)

Efficient and effective training of language and graph neural network models. - Vassilis N. Ioannidis; Xiang Song 003; Da Zheng 004; Houyu Zhang; Jun Ma 029; Yi Xu 011; Belinda Zeng; Trishul Chilimbi; George Karypis (CoRR, 2022)

From Local to Global: Spectral-Inspired Graph Neural Networks. - Ningyuan Huang; Soledad Villar; Carey E. Priebe; Da Zheng 004; Chengyue Huang; Lin Yang 011; Vladimir Braverman (CoRR, 2022)

TGL: A General Framework for Temporal GNN Training onBillion-Scale Graphs. - Hongkuan Zhou; Da Zheng 004; Israt Nisa; Vassilis N. Ioannidis; Xiang Song 003; George Karypis (Proc. VLDB Endow., 2022)

Optimizing Irregular Dense Operators of Heterogeneous GNN Models on GPU. - Israt Nisa; Minjie Wang; Da Zheng 004; Qiang Fu; Ümit V. Çatalyürek; George Karypis (IPDPS Workshops, 2023)

Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications. - Han Xie; Da Zheng 004; Jun Ma 029; Houyu Zhang; Vassilis N. Ioannidis; Xiang Song 003; Qing Ping; Sheng Wang 012; Carl Yang 001; Yi Xu 011; Belinda Zeng; Trishul Chilimbi (KDD, 2023)

GraphStorm an Easy-to-use and Scalable Graph Neural Network Framework: From Beginners to Heroes. - Jian Zhang; Da Zheng 004; Xiang Song 003; Theodore Vasiloudis; Israt Nisa; Jim Lu (KDD, 2023)

Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs. - Costas Mavromatis; Vassilis N. Ioannidis; Shen Wang 005; Da Zheng 004; Soji Adeshina; Jun Ma 029; Han Zhao 002; Christos Faloutsos; George Karypis (ECML/PKDD (3), 2023)

DSP: Efficient GNN Training with Multiple GPUs. - Zhenkun Cai; Qihui Zhou; Xiao Yan 002; Da Zheng 004; Xiang Song 003; Chenguang Zheng; James Cheng; George Karypis (PPoPP, 2023)

TANGO: re-thinking quantization for graph neural network training on GPUs. - Shiyang Chen 004; Da Zheng 004; Caiwen Ding; Chengying Huan; Yuede Ji; Hang Liu 001 (SC, 2023)

DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training. - Hongkuan Zhou; Da Zheng 004; Xiang Song 003; George Karypis; Viktor K. Prasanna (SC, 2023)

PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction. - Shichang Zhang; Jiani Zhang 003; Xiang Song 003; Soji Adeshina; Da Zheng 004; Christos Faloutsos; Yizhou Sun (WWW, 2023)

PIGEON: Optimizing CUDA Code Generator for End-to-End Training and Inference of Relational Graph Neural Networks. - Kun Wu 002; Mert Hidayetoglu; Xiang Song 003; Sitao Huang; Da Zheng 004; Israt Nisa; Wen-Mei W. Hwu (CoRR, 2023)

OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization. - Hengrui Zhang; Shen Wang 005; Vassilis N. Ioannidis; Soji Adeshina; Jiani Zhang 003; Xiao Qin 003; Christos Faloutsos; Da Zheng 004; George Karypis; Philip S. Yu (CoRR, 2023)

Tango: rethinking quantization for graph neural network training on GPUs. - Shiyang Chen 004; Da Zheng 004; Caiwen Ding; Chengying Huan; Yuede Ji; Hang Liu 001 (CoRR, 2023)

RAP: Resource-aware Automated GPU Sharing for Multi-GPU Recommendation Model Training and Input Preprocessing. - Zheng Wang 075; Yuke Wang; Jiaqi Deng; Da Zheng 004; Ang Li 006; Yufei Ding 001 (ASPLOS (2), 2024)

Hector: An Efficient Programming and Compilation Framework for Implementing Relational Graph Neural Networks in GPU Architectures. - Kun Wu 002; Mert Hidayetoglu; Xiang Song 003; Sitao Huang; Da Zheng 004; Israt Nisa; Wen-Mei Hwu (ASPLOS (3), 2024)

Revisit Orthogonality in Graph-Regularized MLPs. - Hengrui Zhang; Shen Wang 005; Vassilis N. Ioannidis; Soji Adeshina; Jiani Zhang 003; Xiao Qin 003; Christos Faloutsos; Da Zheng 004; George Karypis; Philip S. Yu (CIKM, 2024)

NetInfoF Framework: Measuring and Exploiting Network Usable Information. - Meng-Chieh Lee; Haiyang Yu; Jian Zhang; Vassilis N. Ioannidis; Xiang Song 003; Soji Adeshina; Da Zheng 004; Christos Faloutsos (ICLR, 2024)

GraphStorm: All-in-one Graph Machine Learning Framework for Industry Applications. - Da Zheng 004; Xiang Song 003; Qi Zhu 008; Jian Zhang; Theodore Vasiloudis; Runjie Ma; Houyu Zhang; Zichen Wang 002; Soji Adeshina; Israt Nisa; Alejandro Mottini; Qingjun Cui; et al. (KDD, 2024)

Parameter-Efficient Tuning Large Language Models for Graph Representation Learning. - Qi Zhu 008; Da Zheng 004; Xiang Song 003; Shichang Zhang; Bowen Jin; Yizhou Sun; George Karypis (CoRR, 2024)

GraphStorm: all-in-one graph machine learning framework for industry applications. - Da Zheng 004; Xiang Song 003; Qi Zhu 008; Jiani Zhang 003; Theodore Vasiloudis; Runjie Ma; Houyu Zhang; Zichen Wang 002; Soji Adeshina; Israt Nisa; Alejandro Mottini; Qingjun Cui; et al. (CoRR, 2024)

Hierarchical Compression of Text-Rich Graphs via Large Language Models. - Shichang Zhang; Da Zheng 004; Jiani Zhang 003; Qi Zhu 008; Xiang Song 003; Soji Adeshina; Christos Faloutsos; George Karypis; Yizhou Sun (CoRR, 2024)

TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting. - Zonghan Wu; Da Zheng 004; Shirui Pan; Quan Gan; Guodong Long; George Karypis (IEEE Trans. Neural Networks Learn. Syst., 2024)

OneKE: A Dockerized Schema-Guided LLM Agent-based Knowledge Extraction System. - Yujie Luo; Xiangyuan Ru; Kangwei Liu 002; Lin Yuan; Mengshu Sun; Ningyu Zhang 001; Lei Liang 002; Zhiqiang Zhang 012; Jun Zhou 011; Lanning Wei; Da Zheng 004; Haofen Wang; et al. (WWW (Companion Volume), 2025)

LightThinker: Thinking Step-by-Step Compression. - Jintian Zhang; Yuqi Zhu; Mengshu Sun; Yujie Luo; Shuofei Qiao; Lun Du; Da Zheng 004; Huajun Chen; Ningyu Zhang 001 (CoRR, 2025)

Knowledge Augmented Complex Problem Solving with Large Language Models: A Survey. - Da Zheng 004; Lun Du; Junwei Su; Yuchen Tian; Yuqi Zhu; Jintian Zhang; Lanning Wei; Ningyu Zhang 001; Huajun Chen (CoRR, 2025)

Right Is Not Enough: The Pitfalls of Outcome Supervision in Training LLMs for Math Reasoning. - Jiaxing Guo; Wenjie Yang 006; Shengzhong Zhang; Tongshan Xu; Lun Du; Da Zheng 004; Zengfeng Huang (CoRR, 2025)

AutoMind: Adaptive Knowledgeable Agent for Automated Data Science. - Yixin Ou; Yujie Luo; Jingsheng Zheng; Lanning Wei; Shuofei Qiao; Jintian Zhang; Da Zheng 004; Huajun Chen; Ningyu Zhang 001 (CoRR, 2025)

Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Study. - Yuqi Zhu; Yi Zhong; Jintian Zhang; Ziheng Zhang; Shuofei Qiao; Yujie Luo; Lun Du; Da Zheng 004; Huajun Chen; Ningyu Zhang 001 (CoRR, 2025)

Reflective Paper-to-Code Reproduction Enabled by Fine-Grained Verification. - Mingyang Zhou; Quanming Yao; Lun Du; Lanning Wei; Da Zheng 004 (CoRR, 2025)

LLaDA-MoE: A Sparse MoE Diffusion Language Model. - Fengqi Zhu; Zebin You; Yipeng Xing; Zenan Huang; Lin Liu; Yihong Zhuang; Guoshan Lu; Kangyu Wang; Xudong Wang; Lanning Wei; Hongrui Guo; Jiaqi Hu; et al. (CoRR, 2025)

EvolProver: Advancing Automated Theorem Proving by Evolving Formalized Problems via Symmetry and Difficulty. - Yuchen Tian; Ruiyuan Huang; Xuanwu Wang; Jing Ma 004; Zengfeng Huang; Ziyang Luo; Hongzhan Lin 001; Da Zheng 004; Lun Du (CoRR, 2025)

dInfer: An Efficient Inference Framework for Diffusion Language Models. - Yuxin Ma; Lun Du; Lanning Wei; Kun Chen; Qian Xu; Kangyu Wang; Guofeng Feng; Guoshan Lu; Lin Liu; Xiaojing Qi; Xinyuan Zhang; Zhen Tao; et al. (CoRR, 2025)

Executable Knowledge Graphs for Replicating AI Research. - Yujie Luo; Zhuoyun Yu; Xuehai Wang; Yuqi Zhu; Ningyu Zhang 001; Lanning Wei; Lun Du; Da Zheng 004; Huajun Chen (CoRR, 2025)