Search Results for author: Da Zheng

Found 25 papers, 13 papers with code

OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization

no code implementations31 Jan 2023 Hengrui Zhang, Shen Wang, Vassilis N. Ioannidis, Soji Adeshina, Jiani Zhang, Xiao Qin, Christos Faloutsos, Da Zheng, George Karypis, Philip S. Yu

Graph Neural Networks (GNNs) are currently dominating in modeling graph-structure data, while their high reliance on graph structure for inference significantly impedes them from widespread applications.

Node Classification

PIGEON: Optimizing CUDA Code Generator for End-to-End Training and Inference of Relational Graph Neural Networks

no code implementations16 Jan 2023 Kun Wu, Mert Hidayetoğlu, Xiang Song, Sitao Huang, Da Zheng, Israt Nisa, Wen-mei Hwu

Relational graph neural networks (RGNNs) are graph neural networks (GNNs) with dedicated structures for modeling the different types of nodes and/or edges in heterogeneous graphs.

Efficient and effective training of language and graph neural network models

no code implementations22 Jun 2022 Vassilis N. Ioannidis, Xiang Song, Da Zheng, Houyu Zhang, Jun Ma, Yi Xu, Belinda Zeng, Trishul Chilimbi, George Karypis

The effectiveness in our framework is achieved by applying stage-wise fine-tuning of the BERT model first with heterogenous graph information and then with a GNN model.

Edge Classification Language Modelling +1

Nimble GNN Embedding with Tensor-Train Decomposition

no code implementations21 Jun 2022 Chunxing Yin, Da Zheng, Israt Nisa, Christos Faloutos, George Karypis, Richard Vuduc

This paper describes a new method for representing embedding tables of graph neural networks (GNNs) more compactly via tensor-train (TT) decomposition.

graph partitioning

Dr. Top-k: Delegate-Centric Top-k on GPUs

no code implementations16 Sep 2021 Anil Gaihre, Da Zheng, Scott Weitze, Lingda Li, Shuaiwen Leon Song, Caiwen Ding, Xiaoye S Li, Hang Liu

Recent top-$k$ computation efforts explore the possibility of revising various sorting algorithms to answer top-$k$ queries on GPUs.

TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting

1 code implementation25 Aug 2021 Zonghan Wu, Da Zheng, Shirui Pan, Quan Gan, Guodong Long, George Karypis

This paper aims to unify spatial dependency and temporal dependency in a non-Euclidean space while capturing the inner spatial-temporal dependencies for traffic data.

Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs

no code implementations11 Jun 2021 Jialin Dong, Da Zheng, Lin F. Yang, Geroge Karypis

This global cache allows in-GPU importance sampling of mini-batches, which drastically reduces the number of nodes in a mini-batch, especially in the input layer, to reduce data copy between CPU and GPU and mini-batch computation without compromising the training convergence rate or model accuracy.

Fraud Detection

Schema-Aware Deep Graph Convolutional Networks for Heterogeneous Graphs

no code implementations3 May 2021 Saurav Manchanda, Da Zheng, George Karypis

To address this question, we propose our GCN framework 'Deep Heterogeneous Graph Convolutional Network (DHGCN)', which takes advantage of the schema of a heterogeneous graph and uses a hierarchical approach to effectively utilize information many hops away.

Learning over Families of Sets -- Hypergraph Representation Learning for Higher Order Tasks

no code implementations19 Jan 2021 Balasubramaniam Srinivasan, Da Zheng, George Karypis

In this work, we exploit the incidence structure to develop a hypergraph neural network to learn provably expressive representations of variable sized hyperedges which preserve local-isomorphism in the line graph of the hypergraph, while also being invariant to permutations of its constituent vertices.

Graph Representation Learning hyperedge classification

DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs

1 code implementation11 Oct 2020 Da Zheng, Chao Ma, Minjie Wang, Jinjing Zhou, Qidong Su, Xiang Song, Quan Gan, Zheng Zhang, George Karypis

To minimize the overheads associated with distributed computations, DistDGL uses a high-quality and light-weight min-cut graph partitioning algorithm along with multiple balancing constraints.

Fraud Detection graph partitioning

PanRep: Universal node embeddings for heterogeneous graphs

no code implementations28 Sep 2020 Vassilis N. Ioannidis, Da Zheng, George Karypis

Learning unsupervised node embeddings facilitates several downstream tasks such as node classification and link prediction.

Link Prediction Node Classification

FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems

no code implementations26 Aug 2020 Yuwei Hu, Zihao Ye, Minjie Wang, Jiali Yu, Da Zheng, Mu Li, Zheng Zhang, Zhiru Zhang, Yida Wang

FeatGraph provides a flexible programming interface to express diverse GNN models by composing coarse-grained sparse templates with fine-grained user-defined functions (UDFs) on each vertex/edge.

PanRep: Graph neural networks for extracting universal node embeddings in heterogeneous graphs

1 code implementation20 Jul 2020 Vassilis N. Ioannidis, Da Zheng, George Karypis

Learning unsupervised node embeddings facilitates several downstream tasks such as node classification and link prediction.

Link Prediction Node Classification

Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing

1 code implementation20 Jul 2020 Vassilis N. Ioannidis, Da Zheng, George Karypis

This paper proposes an inductive RGCN for learning informative relation embeddings even in the few-shot learning regime.

Drug Discovery Few-Shot Learning +4

DGL-KE: Training Knowledge Graph Embeddings at Scale

1 code implementation18 Apr 2020 Da Zheng, Xiang Song, Chao Ma, Zeyuan Tan, Zihao Ye, Jin Dong, Hao Xiong, Zheng Zhang, George Karypis

Experiments on knowledge graphs consisting of over 86M nodes and 338M edges show that DGL-KE can compute embeddings in 100 minutes on an EC2 instance with 8 GPUs and 30 minutes on an EC2 cluster with 4 machines with 48 cores/machine.

Distributed, Parallel, and Cluster Computing

Graphyti: A Semi-External Memory Graph Library for FlashGraph

no code implementations7 Jul 2019 Disa Mhembere, Da Zheng, Carey E. Priebe, Joshua T. Vogelstein, Randal Burns

Emerging frameworks avoid the network bottleneck of distributed data with Semi-External Memory (SEM) that uses a single multicore node and operates on graphs larger than memory.

Distributed, Parallel, and Cluster Computing Databases

Supervised Dimensionality Reduction for Big Data

1 code implementation5 Sep 2017 Joshua T. Vogelstein, Eric Bridgeford, Minh Tang, Da Zheng, Christopher Douville, Randal Burns, Mauro Maggioni

To solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will be able to build accurate data-driven inferences.

General Classification Supervised dimensionality reduction

knor: A NUMA-Optimized In-Memory, Distributed and Semi-External-Memory k-means Library

1 code implementation28 Jun 2016 Disa Mhembere, Da Zheng, Carey E. Priebe, Joshua T. Vogelstein, Randal Burns

The \textit{k-means NUMA Optimized Routine} (\textsf{knor}) library has (i) in-memory (\textsf{knori}), (ii) distributed memory (\textsf{knord}), and (iii) semi-external memory (\textsf{knors}) modules that radically improve the performance of k-means for varying memory and hardware budgets.

Distributed, Parallel, and Cluster Computing

FlashR: R-Programmed Parallel and Scalable Machine Learning using SSDs

2 code implementations21 Apr 2016 Da Zheng, Disa Mhembere, Joshua T. Vogelstein, Carey E. Priebe, Randal Burns

R is one of the most popular programming languages for statistics and machine learning, but the R framework is relatively slow and unable to scale to large datasets.

Distributed, Parallel, and Cluster Computing

Semi-External Memory Sparse Matrix Multiplication for Billion-Node Graphs

2 code implementations9 Feb 2016 Da Zheng, Disa Mhembere, Vince Lyzinski, Joshua Vogelstein, Carey E. Priebe, Randal Burns

In contrast, we scale sparse matrix multiplication beyond memory capacity by implementing sparse matrix dense matrix multiplication (SpMM) in a semi-external memory (SEM) fashion; i. e., we keep the sparse matrix on commodity SSDs and dense matrices in memory.

Distributed, Parallel, and Cluster Computing

Active Community Detection in Massive Graphs

2 code implementations30 Dec 2014 Heng Wang, Da Zheng, Randal Burns, Carey Priebe

A canonical problem in graph mining is the detection of dense communities.

Social and Information Networks Physics and Society

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